Packages

Make sure all relevant packages have been installed. These include:

Importing “Wave 1A” datafile

First of all, make sure your working directory is set up so that the csv files you need can be read by R.

We can now proceed to import the datafile “Wave 1A”. The first four rows of the file are not necessary for our analyses as they contain two pilot data from the researchers and empty cells.

library(psych)
library(MASS)
library(tidyr)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.0     ✔ purrr     1.0.1
## ✔ forcats   1.0.0     ✔ readr     2.1.4
## ✔ ggplot2   3.4.1     ✔ stringr   1.5.0
## ✔ lubridate 1.9.2     ✔ tibble    3.1.8
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ ggplot2::%+%()   masks psych::%+%()
## ✖ ggplot2::alpha() masks psych::alpha()
## ✖ dplyr::filter()  masks stats::filter()
## ✖ dplyr::lag()     masks stats::lag()
## ✖ dplyr::select()  masks MASS::select()
## ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(dplyr)
library(htmlwidgets)
library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:MASS':
## 
##     select
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
library(ggsci)
library(ggpubr)
library(ggpattern)
library(jtools)
library(moments)

wave1a <- read.csv("wave1A.csv", comment.char="#", stringsAsFactors=FALSE, na.strings=c("","NA"))
#get dimensions and name of the variables
dim(wave1a)
## [1] 448 271
#[1] 448 271
colnames(wave1a)
##   [1] "StartDate"             "EndDate"               "Status"               
##   [4] "IPAddress"             "Progress"              "Duration..in.seconds."
##   [7] "Finished"              "RecordedDate"          "ResponseId"           
##  [10] "RecipientLastName"     "RecipientFirstName"    "RecipientEmail"       
##  [13] "ExternalReference"     "LocationLatitude"      "LocationLongitude"    
##  [16] "DistributionChannel"   "UserLanguage"          "Q1"                   
##  [19] "Q2"                    "Q3"                    "Q4"                   
##  [22] "Q5"                    "Q8"                    "Q9"                   
##  [25] "Q11"                   "Q12"                   "Q13"                  
##  [28] "Q14"                   "Q15"                   "Q16"                  
##  [31] "Q16_4_TEXT"            "Q17"                   "Q18"                  
##  [34] "Q18_4_TEXT"            "Q19"                   "Q19_5_TEXT"           
##  [37] "Q20"                   "Q21"                   "Q22"                  
##  [40] "Q23"                   "Q23_5_TEXT"            "Q24"                  
##  [43] "Q25"                   "Q26"                   "Q27"                  
##  [46] "Q28"                   "Q29"                   "Q30"                  
##  [49] "Q31"                   "Q32"                   "Q33"                  
##  [52] "Q34"                   "Q35"                   "Q36"                  
##  [55] "Q37"                   "Q38"                   "Q39"                  
##  [58] "Q40"                   "Q41"                   "Q42"                  
##  [61] "Q43"                   "Q44"                   "Q45"                  
##  [64] "Q46"                   "Q47"                   "Q48"                  
##  [67] "Q49_1"                 "Q49_2"                 "Q50"                  
##  [70] "Q51"                   "Q53"                   "Q54_1"                
##  [73] "Q54_2"                 "Q55"                   "Q55_0_TEXT"           
##  [76] "Q56_1"                 "Q56_2"                 "Q57"                  
##  [79] "Q58_1"                 "Q58_2"                 "Q59_1"                
##  [82] "Q59_2"                 "Q60"                   "Q61"                  
##  [85] "Q62"                   "Q217"                  "Q63"                  
##  [88] "Q64"                   "Q65"                   "Q66"                  
##  [91] "Q67"                   "Q68"                   "Q69"                  
##  [94] "Q70"                   "Q71"                   "Q72"                  
##  [97] "Q73"                   "Q74"                   "Q75_1"                
## [100] "Q75_2"                 "Q75_3"                 "Q75_4"                
## [103] "Q75_5"                 "Q75_6"                 "Q75_7"                
## [106] "Q75_8"                 "Q76"                   "Q77"                  
## [109] "Q78"                   "Q79"                   "Q80"                  
## [112] "Q81"                   "Q218"                  "Q82"                  
## [115] "Q83"                   "Q84"                   "Q85"                  
## [118] "Q86"                   "Q87"                   "Q88"                  
## [121] "Q89"                   "Q90"                   "Q91"                  
## [124] "Q92"                   "Q93"                   "Q94"                  
## [127] "Q95"                   "Q96"                   "Q97"                  
## [130] "Q98"                   "Q99"                   "Q100"                 
## [133] "Q101"                  "Q102"                  "Q103"                 
## [136] "Q104"                  "Q105"                  "Q106"                 
## [139] "Q107"                  "Q108"                  "Q109"                 
## [142] "Q110"                  "Q111"                  "Q112"                 
## [145] "Q113"                  "Q114"                  "Q115"                 
## [148] "Q116"                  "Q117"                  "Q118"                 
## [151] "Q119"                  "Q120"                  "Q121"                 
## [154] "Q122"                  "Q123"                  "Q124"                 
## [157] "Q125"                  "Q126"                  "Q127"                 
## [160] "Q128"                  "Q129"                  "Q130"                 
## [163] "Q131"                  "Q131_4_TEXT"           "Q132_1"               
## [166] "Q132_2"                "Q132_3"                "Q132_4"               
## [169] "Q132_5"                "Q132_6"                "Q132_7"               
## [172] "Q133_1"                "Q133_2"                "Q133_3"               
## [175] "Q133_4"                "Q133_5"                "Q133_6"               
## [178] "Q133_7"                "Q133_8"                "Q133_9"               
## [181] "Q136"                  "Q137"                  "Q138"                 
## [184] "Q140_1"                "Q140_2"                "Q140_3"               
## [187] "Q140_4"                "Q140_5"                "Q140_6"               
## [190] "Q140_7"                "Q140_8"                "Q140_9"               
## [193] "Q140_10"               "Q140_11"               "Q140_12"              
## [196] "Q140_13"               "Q140_14"               "Q141"                 
## [199] "Q142"                  "Q143"                  "Q144"                 
## [202] "Q220"                  "Q145"                  "Q146"                 
## [205] "Q148"                  "Q149"                  "Q150"                 
## [208] "Q151"                  "Q152"                  "Q153"                 
## [211] "Q154"                  "Q155"                  "Q156"                 
## [214] "Q157_1"                "Q157_2"                "Q157_3"               
## [217] "Q157_4"                "Q157_5"                "Q157_6"               
## [220] "Q157_7"                "Q159"                  "Q160"                 
## [223] "Q161"                  "Q162"                  "Q163"                 
## [226] "Q164"                  "Q165"                  "Q166"                 
## [229] "Q167"                  "Q168"                  "Q169"                 
## [232] "Q221"                  "Q170"                  "Q171"                 
## [235] "Q172"                  "Q173"                  "Q174"                 
## [238] "Q175"                  "Q176"                  "Q178"                 
## [241] "Q179"                  "Q180"                  "Q181"                 
## [244] "Q182"                  "Q183"                  "Q184"                 
## [247] "Q185"                  "Q186"                  "Q187"                 
## [250] "Q188"                  "Q189"                  "Q190"                 
## [253] "Q191"                  "Q192"                  "Q193"                 
## [256] "Q194"                  "Q195"                  "Q196"                 
## [259] "Q197"                  "Q198"                  "Q199"                 
## [262] "Q200"                  "Q201"                  "Q202"                 
## [265] "Q203"                  "Q204"                  "Q207"                 
## [268] "Q209"                  "Q212"                  "Q214"                 
## [271] "Q216"
#[1] "StartDate"             "EndDate"               "Status"               
# [4] "IPAddress"             "Progress"              #"Duration..in.seconds."
#  [7] "Finished"              "RecordedDate"          #"ResponseId"           
# [10] "RecipientLastName"     "RecipientFirstName"    #"RecipientEmail"       
#[13] "ExternalReference"     "LocationLatitude"      #"LocationLongitude"    
#[16] "DistributionChannel"   "UserLanguage"          "Q1"                   
#[19] "Q2"                    "Q3"                    "Q4"                   
#[22] "Q5"                    "Q8"                    "Q9"                   
#[25] "Q11"                   "Q12"                   "Q13"                  
#[28] "Q14"                   "Q15"                   "Q16"                  
#[31] "Q16_4_TEXT"            "Q17"                   "Q18"                  
#[34] "Q18_4_TEXT"            "Q19"                   "Q19_5_TEXT"           
#[37] "Q20"                   "Q21"                   "Q22"                  
#[40] "Q23"                   "Q23_5_TEXT"            "Q24"                  
#[43] "Q25"                   "Q26"                   "Q27"                  
#[46] "Q28"                   "Q29"                   "Q30"                  
#[49] "Q31"                   "Q32"                   "Q33"                  
#[52] "Q34"                   "Q35"                   "Q36"                  
#[55] "Q37"                   "Q38"                   "Q39"                  
#[58] "Q40"                   "Q41"                   "Q42"                  
#[61] "Q43"                   "Q44"                   "Q45"                  
#[64] "Q46"                   "Q47"                   "Q48"                  
#[67] "Q49_1"                 "Q49_2"                 "Q50"                  
#[70] "Q51"                   "Q53"                   "Q54_1"                
#[73] "Q54_2"                 "Q55"                   "Q55_0_TEXT"           
#[76] "Q56_1"                 "Q56_2"                 "Q57"                  
#[79] "Q58_1"                 "Q58_2"                 "Q59_1"                
#[82] "Q59_2"                 "Q60"                   "Q61"                  
#[85] "Q62"                   "Q217"                  "Q63"                  
#[88] "Q64"                   "Q65"                   "Q66"                  
#[91] "Q67"                   "Q68"                   "Q69"                  
#[94] "Q70"                   "Q71"                   "Q72"                  
#[97] "Q73"                   "Q74"                   "Q75_1"                
#[100] "Q75_2"                 "Q75_3"                 "Q75_4"                
#[103] "Q75_5"                 "Q75_6"                 "Q75_7"                
#[106] "Q75_8"                 "Q76"                   "Q77"                  
#[109] "Q78"                   "Q79"                   "Q80"                  
#[112] "Q81"                   "Q218"                  "Q82"                  
#[115] "Q83"                   "Q84"                   "Q85"                  
#[118] "Q86"                   "Q87"                   "Q88"                  
#[121] "Q89"                   "Q90"                   "Q91"                  
#[124] "Q92"                   "Q93"                   "Q94"                  
#[127] "Q95"                   "Q96"                   "Q97"                  
#[130] "Q98"                   "Q99"                   "Q100"                 
#[133] "Q101"                  "Q102"                  "Q103"                 
#[136] "Q104"                  "Q105"                  "Q106"                 
#139] "Q107"                  "Q108"                  "Q109"                 
#142] "Q110"                  "Q111"                  "Q112"                 
#145] "Q113"                  "Q114"                  "Q115"                 
#148] "Q116"                  "Q117"                  "Q118"                 
#151] "Q119"                  "Q120"                  "Q121"                 
#154] "Q122"                  "Q123"                  "Q124"                 
#157] "Q125"                  "Q126"                  "Q127"                 
#160] "Q128"                  "Q129"                  "Q130"                 
#163] "Q131"                  "Q131_4_TEXT"           "Q132_1"              
#166] "Q132_2"                "Q132_3"                "Q132_4"             
#169] "Q132_5"                "Q132_6"                "Q132_7"               
#172] "Q133_1"                "Q133_2"                "Q133_3"               
#175] "Q133_4"                "Q133_5"                "Q133_6"               
#178] "Q133_7"                "Q133_8"                "Q133_9"               
#181] "Q136"                  "Q137"                  "Q138"                 
#184] "Q140_1"                "Q140_2"                "Q140_3"               
#187] "Q140_4"                "Q140_5"                "Q140_6"               
#190] "Q140_7"                "Q140_8"                "Q140_9"              
#193] "Q140_10"               "Q140_11"               "Q140_12"              
#196] "Q140_13"               "Q140_14"               "Q141"                 
#199] "Q142"                  "Q143"                  "Q144"                 
#202] "Q220"                  "Q145"                  "Q146"                 
#205] "Q148"                  "Q149"                  "Q150"                 
#208] "Q151"                  "Q152"                  "Q153"                 
#211] "Q154"                  "Q155"                  "Q156"                 
#214] "Q157_1"                "Q157_2"                "Q157_3"               
#217] "Q157_4"                "Q157_5"                "Q157_6"               
#220] "Q157_7"                "Q159"                  "Q160"                 
#223] "Q161"                  "Q162"                  "Q163"                 
#226] "Q164"                  "Q165"                  "Q166"                 
#229] "Q167"                  "Q168"                  "Q169"                 
#232] "Q221"                  "Q170"                  "Q171"                 
#235] "Q172"                  "Q173"                  "Q174"                 
#238] "Q175"                  "Q176"                  "Q178"                 
#241] "Q179"                  "Q180"                  "Q181"                 
#244] "Q182"                  "Q183"                  "Q184"                 
#247] "Q185"                  "Q186"                  "Q187"                 
#250] "Q188"                  "Q189"                  "Q190"                 
#253] "Q191"                  "Q192"                  "Q193"                 
#256 "Q194"                  "Q195"                  "Q196"                 
#259] "Q197"                  "Q198"                  "Q199"                 
#262] "Q200"                  "Q201"                  "Q202"                 
#265] "Q203"                  "Q204"                  "Q207"                 
#268] "Q209"                  "Q212"                  "Q214"                 
#271] "Q216"  

#remove the first four rows(string + two pilot participants)
wave1 <- wave1a[-c(1,2,3,4),]

#check for head and dimensions of the datafile

head(wave1)
wc <- data.frame(colnames(wave1a))

knitr::kable(wc)
colnames.wave1a.
StartDate
EndDate
Status
IPAddress
Progress
Duration..in.seconds.
Finished
RecordedDate
ResponseId
RecipientLastName
RecipientFirstName
RecipientEmail
ExternalReference
LocationLatitude
LocationLongitude
DistributionChannel
UserLanguage
Q1
Q2
Q3
Q4
Q5
Q8
Q9
Q11
Q12
Q13
Q14
Q15
Q16
Q16_4_TEXT
Q17
Q18
Q18_4_TEXT
Q19
Q19_5_TEXT
Q20
Q21
Q22
Q23
Q23_5_TEXT
Q24
Q25
Q26
Q27
Q28
Q29
Q30
Q31
Q32
Q33
Q34
Q35
Q36
Q37
Q38
Q39
Q40
Q41
Q42
Q43
Q44
Q45
Q46
Q47
Q48
Q49_1
Q49_2
Q50
Q51
Q53
Q54_1
Q54_2
Q55
Q55_0_TEXT
Q56_1
Q56_2
Q57
Q58_1
Q58_2
Q59_1
Q59_2
Q60
Q61
Q62
Q217
Q63
Q64
Q65
Q66
Q67
Q68
Q69
Q70
Q71
Q72
Q73
Q74
Q75_1
Q75_2
Q75_3
Q75_4
Q75_5
Q75_6
Q75_7
Q75_8
Q76
Q77
Q78
Q79
Q80
Q81
Q218
Q82
Q83
Q84
Q85
Q86
Q87
Q88
Q89
Q90
Q91
Q92
Q93
Q94
Q95
Q96
Q97
Q98
Q99
Q100
Q101
Q102
Q103
Q104
Q105
Q106
Q107
Q108
Q109
Q110
Q111
Q112
Q113
Q114
Q115
Q116
Q117
Q118
Q119
Q120
Q121
Q122
Q123
Q124
Q125
Q126
Q127
Q128
Q129
Q130
Q131
Q131_4_TEXT
Q132_1
Q132_2
Q132_3
Q132_4
Q132_5
Q132_6
Q132_7
Q133_1
Q133_2
Q133_3
Q133_4
Q133_5
Q133_6
Q133_7
Q133_8
Q133_9
Q136
Q137
Q138
Q140_1
Q140_2
Q140_3
Q140_4
Q140_5
Q140_6
Q140_7
Q140_8
Q140_9
Q140_10
Q140_11
Q140_12
Q140_13
Q140_14
Q141
Q142
Q143
Q144
Q220
Q145
Q146
Q148
Q149
Q150
Q151
Q152
Q153
Q154
Q155
Q156
Q157_1
Q157_2
Q157_3
Q157_4
Q157_5
Q157_6
Q157_7
Q159
Q160
Q161
Q162
Q163
Q164
Q165
Q166
Q167
Q168
Q169
Q221
Q170
Q171
Q172
Q173
Q174
Q175
Q176
Q178
Q179
Q180
Q181
Q182
Q183
Q184
Q185
Q186
Q187
Q188
Q189
Q190
Q191
Q192
Q193
Q194
Q195
Q196
Q197
Q198
Q199
Q200
Q201
Q202
Q203
Q204
Q207
Q209
Q212
Q214
Q216
print(dim(wave1))
## [1] 444 271

Secondly, as I (LC) was an undergraduate student while my supervisors (CCYW and HZ) ran the study, I had to delete column which could make participants identifiable. As such I removed two columns from the file.

#remove k-number and emails for confidentiality

wave1 <- within(wave1, rm(Q14, Q15))
dim(wave1)
## [1] 444 269
#[1] 444 269

Creating the Variables

I computed all scores for each questionnaire used and relabelled descriptive statistics and other categorical variables.

# Creating the variables ####
library(psych)
library(MASS)
library(tidyr)
library(tidyverse)
library(dplyr)
library(htmlwidgets)
library(plotly)
library(ggsci)
library(ggpubr)
library(ggpattern)
library(jtools)
library(moments)
#first questions

#Kings student?
wave1$KCL <- recode(wave1$Q1, "1" = "yes", "2" = "no", "3" = "graduate")
head(wave1$KCL)
## [1] "yes" "yes" "yes" "yes" "yes" "yes"
#1] "yes" "yes" "yes" "yes" "yes" "yes"

#over 18?
wave1$age18 <- recode(wave1$Q2, "1"="yes", "2"="no")
head(wave1$age18)
## [1] "yes" "yes" "yes" "yes" "yes" "yes"
#1] "yes" "yes" "yes" "yes" "yes" "yes"

#undergrad or postgrad? 
wave1$UGPG <- recode(wave1$Q3, "1"="UG", "2"="PG")
head(wave1$UGPG)
## [1] "PG" "PG" "UG" "PG" "UG" "UG"
#1] "PG" "PG" "UG" "PG" "UG" "UG"

#year 

wave1$year <- wave1$Q4
head(wave1$year)
## [1] "4" "3" "1" "1" "1" "4"
#1] "4" "3" "1" "1" "1" "4"

#faculty
wave1$faculty <- recode(wave1$Q5, "1"="A&H", "2"="Dentistry", "3"="LSM", "4"="NMS", "5"="Nursing", "6"="IoPPN", "7"="Business", "8"="Law", "9"="SSPP")
head(wave1$faculty)
## [1] "LSM"   "LSM"   "A&H"   "IoPPN" "IoPPN" "LSM"
#1] "LSM"   "LSM"   "A&H"   "IoPPN" "IoPPN" "LSM"  

#Demographics


#gender 

wave1$gender <- recode(wave1$Q16, "1"="male", "2"="female", "3"="non-binary", "4"="other", "5"="prefer not to say")
head(wave1$gender)
## [1] "female" "female" "female" "female" "female" "female"
#[1] "female" "female" "female" "female" "female" "female"


#transgender

wave1$transgender <- recode(wave1$Q17, "1"="transgender", "2"="cisgender")
head(wave1$transgender)
## [1] "cisgender" "cisgender" "cisgender" "cisgender" "cisgender" "cisgender"
#[1] "cisgender" "cisgender" "cisgender" "cisgender" "cisgender" "cisgender"


#sexual orientation - NOTE this needs to be recoded in next version because we added some options

wave1$orientation<- recode(wave1$Q18, "1"="heterosexual", "2"="mostly straight", "3"="bisexual", "6" = "mostly gay", "7"="gay/lesbian", "4" = "other", "5" ="prefer not to say")
head(wave1$orientation)
## [1] "heterosexual"      "heterosexual"      "mostly straight"  
## [4] "bisexual"          "prefer not to say" "heterosexual"
#[1] "heterosexual"      "heterosexual"      "mostly straight"   "bisexual"          "prefer not to say" "heterosexual"     


#ethnicity

wave1$ethnicity<- recode(wave1$Q19, "1"="White", "2"="Asian", "3"="Black", "4"="Mixed", "5"="Other", "6"="prefer not to say")
head(wave1$ethnicity)
## [1] "White" "White" "White" "White" "Mixed" "White"
#[1] "White" "White" "White" "White" "Mixed" "White"


#student status

wave1$student.status <- recode(wave1$Q21, "1"="home", "2"="EU", "3"="overseas")
head(wave1$student.status)
## [1] "EU"       "EU"       "home"     "overseas" "home"     "home"
#[1] "EU"       "EU"       "home"     "overseas" "home"     "home"    


#age

wave1$age <- wave1$Q20


#disability

wave1$disability <- recode(wave1$Q22, "1"="disability", "2"="no disability", "3"="prefer not to say")
head(wave1$disability)
## [1] "no disability" "disability"    "no disability" "disability"   
## [5] "no disability" "no disability"
#[1] "no disability" "disability"    "no disability" "disability"    "no disability" "no disability"


#learning disability - NOTE needs to be recoded to allow for multiple selections. Code as LD / no-LD?

wave1$LD <- recode(wave1$Q23, "1"="dyslexia", "2"="dyspraxia", "3"="ADD/ADHD", "4"="ASD", "5"="other", "6"="none")
head(wave1$LD)
## [1] "none" "none" "none" NA     "none" "none"
#[1] "none" "none" "none" NA     "none" "none"

#-  Accommodation Q24 - column 42
# grepl return whether x-value is in the cells of a specified column

wave1$accomodation <- recode(wave1$Q24, "1"="halls", "2"="rented", "3"="family", "4"="own", "5"="other" )
head(wave1$accomodation)
## [1] "rented" "rented" "other"  "halls"  "halls"  "rented"
wave1$hm_KCL <- grepl("1", wave1$Q25)
wave1$hm_students <- grepl("2", wave1$Q25)
wave1$hm_friends <- grepl("3", wave1$Q25)
wave1$hm_family <- grepl("4", wave1$Q25)
wave1$hm_partner <- grepl("5", wave1$Q25)
wave1$hm_alone <- grepl("6", wave1$Q25)
wave1$hm_other <- grepl("7", wave1$Q25)

head(wave1$hm_other)
## [1]  TRUE FALSE FALSE FALSE FALSE FALSE
#[1]  TRUE FALSE FALSE FALSE FALSE FALSE


#commute - Q27


wave1$commute <- recode(wave1$Q27, "1"="15", "2"="30", "3"="45", "4"="60", 
                        "5"="75", "6"="90","7"="105", "8"="120+")
head(wave1$commute)
## [1] "30"  "30"  "105" "15"  "15"  "30"
#[1] "30"  "30"  "105" "15"  "15"  "30" 


#-Job - Q29

wave1$employed <- recode(wave1$Q29, "1"="yes", "2"="no")
head(wave1$employed)
## [1] "no"  "no"  "no"  "no"  "no"  "yes"
#[1] "no"  "no"  "no"  "no"  "no"  "yes"

wave1$Q31 <- recode(wave1$Q31, "1"="no interference", "2"="some interference", "3"="interference")
head(wave1$Q31)
## [1] NA                NA                NA                NA               
## [5] NA                "no interference"
#[1] NA                NA                NA                NA                NA                "no interference"

#Past mental health - Q83 = past mental distress? column number 111

wave1$Q83<- recode(wave1$Q83, "1"="yes", "2"="no", "3"="prefer not to say")
head(wave1$Q83)
## [1] "no"  "yes" "yes" "yes" "yes" "yes"
#[1] "no"  "yes" "yes" "yes" "yes" "yes"

#Q84 - past help-seeking. Column number 112
wave1$Q84<- recode(wave1$Q84, "1"="yes", "2"="no", "3"="prefer not to say")
head(wave1$Q84)
## [1] "no"  "yes" "yes" "yes" "yes" "yes"
#[1] "no"  "yes" "yes" "yes" "yes" "yes"

#make new column for each diagnosis with true / false if that diagnosis is selected
#did not wrote the code that wasn’t working for Lauren

wave1$diag_depression <- grepl("1", wave1$Q85)
wave1$diag_mania <- grepl("2", wave1$Q85)
wave1$diag_GAD <- grepl("3", wave1$Q85)
wave1$diag_socialanx <- grepl("4", wave1$Q85)
wave1$diag_agoraphobia <- grepl("5", wave1$Q85)
wave1$diag_panic <- grepl("6", wave1$Q85)
wave1$diag_OCD <- grepl("7", wave1$Q85)
wave1$diag_anorexia <- grepl("8", wave1$Q85)
wave1$diag_bulimia <- grepl("9", wave1$Q85)
wave1$diag_binge <- grepl("10", wave1$Q85)
wave1$diag_schizophrenia <- grepl("11", wave1$Q85)
wave1$diag_psychosis <- grepl("12", wave1$Q85)
wave1$diag_PD <- grepl("13", wave1$Q85)
wave1$diag_autism <- grepl("14", wave1$Q85)
wave1$diag_ADHD <- grepl("15", wave1$Q85)

#making a binary variable of past diagnosis

#recoding past mental health diagnoses - columns 302 - 316

wave1$past.diagnosis <- FALSE
wave1$past.diagnosis[wave1$diag_depression == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_mania == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_GAD == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_socialanx == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_agoraphobia == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_panic == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_OCD == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_anorexia == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_bulimia == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_binge == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_schizophrenia == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_psychosis == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_PD == TRUE]<- TRUE
wave1$past.diagnosis[wave1$diag_autism == TRUE] <- TRUE
wave1$past.diagnosis[wave1$diag_ADHD == TRUE] <- TRUE
head(wave1$past.diagnosis)
## [1] FALSE  TRUE  TRUE  TRUE  TRUE FALSE
## [1] FALSE  TRUE  TRUE  TRUE  TRUE FALSE

#-  Age and relation to uni 
wave1$Q87<- recode(wave1$Q87, "1"="before", "2"="after")
head(wave1$Q87)
## [1] NA       "after"  "before" NA       NA       NA
#[1] NA       "after"  "before" NA       NA       NA


wave1$Q88<- recode(wave1$Q88, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q88)
## [1] NA      "worse" "worse" NA      NA      NA
#[1] NA      "worse" "worse" NA      NA      NA     


wave1$Q90<- recode(wave1$Q90, "1"="before", "2"="after")
head(wave1$Q90)
## [1] NA       NA       "before" NA       NA       NA
#[1] NA       NA       "before" NA       NA       NA      


wave1$Q91<- recode(wave1$Q91, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q91)
## [1] NA      NA      "worse" NA      NA      NA
#[1] NA      NA      "worse" NA      NA      NA     

wave1$Q93<- recode(wave1$Q93, "1"="before", "2"="after")
head(wave1$Q93)
## [1] NA       "after"  "before" "before" "before" NA
#[1] NA       "after"  "before" "before" "before" NA

wave1$Q94<- recode(wave1$Q94, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q94)
## [1] NA      "worse" "worse" "worse" "same"  NA
#[1] NA      "worse" "worse" "worse" "same"  NA      

wave1$Q96<- recode(wave1$Q96, "1"="before", "2"="after")
head(wave1$Q96)
## [1] NA       NA       "before" "before" NA       NA
#[1] NA       NA       "before" "before" NA       NA

wave1$Q97<- recode(wave1$Q97, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q97)
## [1] NA     NA     "same" "same" NA     NA
#[1] NA     NA     "same" "same" NA     NA   

wave1$Q99<- recode(wave1$Q99, "1"="before", "2"="after")
head(wave1$Q99)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q100<- recode(wave1$Q100, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q100)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q102<- recode(wave1$Q102, "1"="before", "2"="after")
head(wave1$Q102)
## [1] NA       NA       "before" "before" NA       NA
#[1] NA       NA       "before" "before" NA       NA     

wave1$Q103<- recode(wave1$Q103, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q103)
## [1] NA     NA     "same" "same" NA     NA
#[1] NA     NA     "same" "same" NA     NA    

wave1$Q105<- recode(wave1$Q105, "1"="before", "2"="after")
head(wave1$Q105)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q106<- recode(wave1$Q106, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q106)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA


wave1$Q108<- recode(wave1$Q108, "1"="before", "2"="after")
head(wave1$Q108)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q109<- recode(wave1$Q109, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q109)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA


wave1$Q111<- recode(wave1$Q111, "1"="before", "2"="after")
head(wave1$Q111)
## [1] NA       NA       "before" NA       NA       NA
#[1] NA       NA       "before" NA       NA       NA    
wave1$Q112<- recode(wave1$Q112, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q112)
## [1] NA         NA         "improved" NA         NA         NA
#[1] NA         NA         "improved" NA         NA         NA      

wave1$Q114<- recode(wave1$Q114, "1"="before", "2"="after")
head(wave1$Q114)
## [1] NA       NA       "before" NA       NA       NA
#[1] NA       NA       "before" NA       NA       NA    

wave1$Q115<- recode(wave1$Q115, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q115)
## [1] NA         NA         "improved" NA         NA         NA
#[1] NA         NA         "improved" NA         NA         NA   

wave1$Q117<- recode(wave1$Q117, "1"="before", "2"="after")
head(wave1$Q117)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q118<- recode(wave1$Q118, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q118)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q120<- recode(wave1$Q120, "1"="before", "2"="after")
head(wave1$Q120)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q121<- recode(wave1$Q121, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q121)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q123<- recode(wave1$Q123, "1"="before", "2"="after")
head(wave1$Q123)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA
wave1$Q124<- recode(wave1$Q124, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q124)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q126<- recode(wave1$Q126, "1"="before", "2"="after")
head(wave1$Q126)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q127<- recode(wave1$Q127, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q127)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q129<- recode(wave1$Q129, "1"="before", "2"="after")
head(wave1$Q129)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

wave1$Q130<- recode(wave1$Q130, "1"="improved", "2"="same", "3"="worse")
head(wave1$Q130)
## [1] NA NA NA NA NA NA
#[1] NA NA NA NA NA NA

#Treatment Q131
wave1$tx_medication <- grepl("1", wave1$Q131)
wave1$tx_therapy <- grepl("2", wave1$Q131)
wave1$tx_admission <- grepl("3", wave1$Q131)
wave1$tx_other <- grepl("4", wave1$Q131)
wave1$tx_none <- grepl("5", wave1$Q131)
#-Placement - Q32

wave1$placement <- recode(wave1$Q32, "1"="yes", "2"="no")
head(wave1$placement)
## [1] "no" "no" "no" "no" "no" "no"
#[1] "no" "no" "no" "no" "no" "no"


#Social life before uni 

wave1$relocation <- recode(wave1$Q34, "1"="relocated UK", "2"="relocated London", "3"="relocated international", "4"="no",)
head(wave1$relocation)
## [1] "relocated international" "relocated international"
## [3] "no"                      "relocated international"
## [5] "relocated international" "relocated UK"
#[1] "relocated international" "relocated international" "no"                      "relocated international" "relocated international"
#[6] "relocated UK"           

wave1$Q35 <- recode(wave1$Q35, "1"="once a day", "2"="few times a week", "3"="once a week", "4"="few times a month", "5"="once a month or less")
head(wave1$Q35)
## [1] "once a week"      "once a day"       NA                 "once a day"      
## [5] "few times a week" "once a day"
# [1] "once a week"      "once a day"       NA                 "once a day"       "few times a week" "once a day"


#-  UCLA loneliness scale Q36, Q37, Q38

#control the names pf the columns 
colnames(wave1)
##   [1] "StartDate"             "EndDate"               "Status"               
##   [4] "IPAddress"             "Progress"              "Duration..in.seconds."
##   [7] "Finished"              "RecordedDate"          "ResponseId"           
##  [10] "RecipientLastName"     "RecipientFirstName"    "RecipientEmail"       
##  [13] "ExternalReference"     "LocationLatitude"      "LocationLongitude"    
##  [16] "DistributionChannel"   "UserLanguage"          "Q1"                   
##  [19] "Q2"                    "Q3"                    "Q4"                   
##  [22] "Q5"                    "Q8"                    "Q9"                   
##  [25] "Q11"                   "Q12"                   "Q13"                  
##  [28] "Q16"                   "Q16_4_TEXT"            "Q17"                  
##  [31] "Q18"                   "Q18_4_TEXT"            "Q19"                  
##  [34] "Q19_5_TEXT"            "Q20"                   "Q21"                  
##  [37] "Q22"                   "Q23"                   "Q23_5_TEXT"           
##  [40] "Q24"                   "Q25"                   "Q26"                  
##  [43] "Q27"                   "Q28"                   "Q29"                  
##  [46] "Q30"                   "Q31"                   "Q32"                  
##  [49] "Q33"                   "Q34"                   "Q35"                  
##  [52] "Q36"                   "Q37"                   "Q38"                  
##  [55] "Q39"                   "Q40"                   "Q41"                  
##  [58] "Q42"                   "Q43"                   "Q44"                  
##  [61] "Q45"                   "Q46"                   "Q47"                  
##  [64] "Q48"                   "Q49_1"                 "Q49_2"                
##  [67] "Q50"                   "Q51"                   "Q53"                  
##  [70] "Q54_1"                 "Q54_2"                 "Q55"                  
##  [73] "Q55_0_TEXT"            "Q56_1"                 "Q56_2"                
##  [76] "Q57"                   "Q58_1"                 "Q58_2"                
##  [79] "Q59_1"                 "Q59_2"                 "Q60"                  
##  [82] "Q61"                   "Q62"                   "Q217"                 
##  [85] "Q63"                   "Q64"                   "Q65"                  
##  [88] "Q66"                   "Q67"                   "Q68"                  
##  [91] "Q69"                   "Q70"                   "Q71"                  
##  [94] "Q72"                   "Q73"                   "Q74"                  
##  [97] "Q75_1"                 "Q75_2"                 "Q75_3"                
## [100] "Q75_4"                 "Q75_5"                 "Q75_6"                
## [103] "Q75_7"                 "Q75_8"                 "Q76"                  
## [106] "Q77"                   "Q78"                   "Q79"                  
## [109] "Q80"                   "Q81"                   "Q218"                 
## [112] "Q82"                   "Q83"                   "Q84"                  
## [115] "Q85"                   "Q86"                   "Q87"                  
## [118] "Q88"                   "Q89"                   "Q90"                  
## [121] "Q91"                   "Q92"                   "Q93"                  
## [124] "Q94"                   "Q95"                   "Q96"                  
## [127] "Q97"                   "Q98"                   "Q99"                  
## [130] "Q100"                  "Q101"                  "Q102"                 
## [133] "Q103"                  "Q104"                  "Q105"                 
## [136] "Q106"                  "Q107"                  "Q108"                 
## [139] "Q109"                  "Q110"                  "Q111"                 
## [142] "Q112"                  "Q113"                  "Q114"                 
## [145] "Q115"                  "Q116"                  "Q117"                 
## [148] "Q118"                  "Q119"                  "Q120"                 
## [151] "Q121"                  "Q122"                  "Q123"                 
## [154] "Q124"                  "Q125"                  "Q126"                 
## [157] "Q127"                  "Q128"                  "Q129"                 
## [160] "Q130"                  "Q131"                  "Q131_4_TEXT"          
## [163] "Q132_1"                "Q132_2"                "Q132_3"               
## [166] "Q132_4"                "Q132_5"                "Q132_6"               
## [169] "Q132_7"                "Q133_1"                "Q133_2"               
## [172] "Q133_3"                "Q133_4"                "Q133_5"               
## [175] "Q133_6"                "Q133_7"                "Q133_8"               
## [178] "Q133_9"                "Q136"                  "Q137"                 
## [181] "Q138"                  "Q140_1"                "Q140_2"               
## [184] "Q140_3"                "Q140_4"                "Q140_5"               
## [187] "Q140_6"                "Q140_7"                "Q140_8"               
## [190] "Q140_9"                "Q140_10"               "Q140_11"              
## [193] "Q140_12"               "Q140_13"               "Q140_14"              
## [196] "Q141"                  "Q142"                  "Q143"                 
## [199] "Q144"                  "Q220"                  "Q145"                 
## [202] "Q146"                  "Q148"                  "Q149"                 
## [205] "Q150"                  "Q151"                  "Q152"                 
## [208] "Q153"                  "Q154"                  "Q155"                 
## [211] "Q156"                  "Q157_1"                "Q157_2"               
## [214] "Q157_3"                "Q157_4"                "Q157_5"               
## [217] "Q157_6"                "Q157_7"                "Q159"                 
## [220] "Q160"                  "Q161"                  "Q162"                 
## [223] "Q163"                  "Q164"                  "Q165"                 
## [226] "Q166"                  "Q167"                  "Q168"                 
## [229] "Q169"                  "Q221"                  "Q170"                 
## [232] "Q171"                  "Q172"                  "Q173"                 
## [235] "Q174"                  "Q175"                  "Q176"                 
## [238] "Q178"                  "Q179"                  "Q180"                 
## [241] "Q181"                  "Q182"                  "Q183"                 
## [244] "Q184"                  "Q185"                  "Q186"                 
## [247] "Q187"                  "Q188"                  "Q189"                 
## [250] "Q190"                  "Q191"                  "Q192"                 
## [253] "Q193"                  "Q194"                  "Q195"                 
## [256] "Q196"                  "Q197"                  "Q198"                 
## [259] "Q199"                  "Q200"                  "Q201"                 
## [262] "Q202"                  "Q203"                  "Q204"                 
## [265] "Q207"                  "Q209"                  "Q212"                 
## [268] "Q214"                  "Q216"                  "KCL"                  
## [271] "age18"                 "UGPG"                  "year"                 
## [274] "faculty"               "gender"                "transgender"          
## [277] "orientation"           "ethnicity"             "student.status"       
## [280] "age"                   "disability"            "LD"                   
## [283] "accomodation"          "hm_KCL"                "hm_students"          
## [286] "hm_friends"            "hm_family"             "hm_partner"           
## [289] "hm_alone"              "hm_other"              "commute"              
## [292] "employed"              "diag_depression"       "diag_mania"           
## [295] "diag_GAD"              "diag_socialanx"        "diag_agoraphobia"     
## [298] "diag_panic"            "diag_OCD"              "diag_anorexia"        
## [301] "diag_bulimia"          "diag_binge"            "diag_schizophrenia"   
## [304] "diag_psychosis"        "diag_PD"               "diag_autism"          
## [307] "diag_ADHD"             "past.diagnosis"        "tx_medication"        
## [310] "tx_therapy"            "tx_admission"          "tx_other"             
## [313] "tx_none"               "placement"             "relocation"
#To use the sapply() function in R, you have to define the List or Vector you want to iterate on the first parameter and the function you want to apply to each vector element in the second argument.
wave1[, c(52:54)] <- sapply(wave1[, c(52:54)], as.numeric, is.na=NA)

str(wave1$Q36)
##  num [1:444] 1 2 3 2 2 1 3 2 1 2 ...
#chr [1:444] "1" "2" "3" "2" "2" "1" "3" "2" "1" "2" "2" "1" "3" "1" "2" "2" "3" "3" "2" "1" "2" "2" "3" "1" "2" "1" "2" "2" "3" "1" "1" "1" "1" "2" ...
head(wave1$Q36)
## [1] 1 2 3 2 2 1
## [1] 1 2 3 2 2 1


#The rowSums() is a built-in R function used to calculate the sum of rows of a matrix or an array. The rowSums() method takes an R Object-like matrix or array and returns the sum of rows.

wave1$pre.loneliness<- rowSums(wave1[,c(52:54)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(52:54)]))

str(wave1$pre.loneliness)
##  num [1:444] 3 6 9 6 6 5 9 4 3 6 ...
#num [1:444] 3 6 9 6 6 5 9 4 3 6 ...
head(wave1$pre.loneliness)
## [1] 3 6 9 6 6 5
#[1] 3 6 9 6 6 5

#Social life at uni 
#-  Questions 

wave1$Q39 <- recode(wave1$Q39, "5"="more than once a day", "4"="once a day", "3"="3-6 times per week", "2"="1-2 times per week", "1"="less than once a week")
head(wave1$Q39)
## [1] "3-6 times per week"    "3-6 times per week"    "less than once a week"
## [4] "1-2 times per week"    "once a day"            "3-6 times per week"
#[1] "3-6 times per week"    "3-6 times per week"    "less than once a week" "1-2 times per week"    "once a day"            "3-6 times per week"   

wave1$Q40 <- recode(wave1$Q40,"7"="more than 10 times a day", "6"="6-10 times a day", "5"="2-5 times a day", "4"="once a day", "3"="3-6 times per week", "2"="1-2 times per week", "1"="less than once a week")
head(wave1$Q40)
## [1] "3-6 times per week"       "2-5 times a day"         
## [3] "1-2 times per week"       "6-10 times a day"        
## [5] "6-10 times a day"         "more than 10 times a day"
#[1] "3-6 times per week"       "2-5 times a day"          "1-2 times per week"       "6-10 times a day"         "6-10 times a day"        
#[6] "more than 10 times a day"

wave1$Q41 <- recode(wave1$Q41, "10"="10+")
head(wave1$Q41)
## [1] "3" "1" "0" "1" "2" "0"
#[1] "3" "1" "0" "1" "2" "0"

#what about42 and 43??
#-  UCLA loneliness scale
#recode column (q45-q47,-2)

wave1[, c(61:63)] <- sapply(wave1[, c(61:63)], as.numeric, is.na=NA)

wave1$uni.loneliness<- rowSums(wave1[,c(61:63)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(61:63)]))


#Relationships Q48-Q51

wave1$Q48B <- recode(wave1$Q48, "1"="single", "2"="dating non-exclusively", "3"="exclusive relationship", "4"="co-habitating", "5"="married", "6"="widowed", "7"="separated", "8"="divorced")
head(wave1$Q48B)
## [1] "co-habitating"          "single"                 "co-habitating"         
## [4] "exclusive relationship" "single"                 "exclusive relationship"
#[1] "co-habitating"          "single"                 "co-habitating"          "exclusive relationship" "single"                 "exclusive relationship"

wave1$relationship.status <- recode(wave1$Q48, "1"="no", "2"="yes", "3"="yes", "4"="yes", "5"="yes", "6"= "no", "7" = "no", "8" ="no")
head(wave1$relationship.status)
## [1] "yes" "no"  "yes" "yes" "no"  "yes"
#[1] "yes" "no"  "yes" "yes" "no"  "yes"


wave1$Q49_1 <- as.numeric(wave1$Q49_1)

wave1$Q49_2 <- as.numeric(wave1$Q49_2)

wave1$relationship.months <- (wave1$Q49_1*12)+wave1$Q49_2
head(wave1$relationship.months)
## [1] 26 NA 25 NA NA 13
#[1] 26 NA 25 NA NA 13

wave1$Q50 <- recode(wave1$Q50, "1"="before", "2"="after")
head(wave1$Q50)
## [1] "after"  NA       "before" "before" NA       "after"
#[1] "after"  NA       "before" "before" NA       "after"


wave1$Q51 <- recode(wave1$Q51, "1"="LDR", "2"="non-LDR")
head(wave1$Q51)
## [1] NA        NA        NA        "LDR"     NA        "non-LDR"
#[1] NA        NA        NA        "LDR"     NA        "non-LDR"


#Physical activity Q53-Q59_2 (69:80)

#vigorous activity Q53-Q54_2- first calculate time in minutes, then multiply by days

wave1[, c(69:71)] <- sapply(wave1[, c(69:71)], as.numeric, is.na=NA)

wave1$v.mins <- (wave1$Q54_1*60) + wave1$Q54_2
head(wave1$v.mins)
## [1] 60 30 NA NA  0 40
#[1] 60 30 NA NA  0 40

wave1$ipaq.v <- wave1$v.mins*wave1$Q53
head(wave1$ipaq.v)
## [1]  60  60  NA  NA   0 120
#[1]  60  60  NA  NA   0 120

#moderate activity Q55 - Q56_2 (col 73 empty)

wave1[, c(72, 74, 75)] <- sapply(wave1[, c(72, 74, 75)], as.numeric, is.na=NA)

wave1$m.mins <- (wave1$Q56_1*60) + wave1$Q56_2
head(wave1$m.mins)
## [1] 120  30  NA  NA   0   0
#[1] 120  30  NA  NA   0   0

wave1$ipaq.m <- wave1$m.mins*wave1$Q55
head(wave1$ipaq.m)
## [1] 360  60  NA  NA   0   0
#[1] 360  60  NA  NA   0   0

#walking Q57 - Q58_2

wave1[, c(76:78)] <- sapply(wave1[, c(76:78)], as.numeric, is.na=NA)

wave1$w.mins <- (wave1$Q58_1*60) + wave1$Q58_2
head(wave1$w.mins)
## [1] 60 30 NA NA 30 60
#[1] 60 30 NA NA 30 60
wave1$ipaq.walk <- wave1$w.mins*wave1$Q55
head(wave1$ipaq.walk)
## [1] 180  60  NA  NA   0   0
#[1] 180  60  NA  NA   0   0

#sitting (per day instead of per week)

wave1[, c(79, 80)] <- sapply(wave1[, c(79, 80)], as.numeric, is.na=NA)

wave1$ipaq.sit <- (wave1$Q59_1*60) + wave1$Q59_2
head(wave1$ipaq.sit)
## [1] 420 420  NA  NA  NA 300
#[1] 420 420  NA  NA  NA 300


#Academic
#-  Questions 

#attendance - Q64 

wave1$Q64 <- recode(wave1$Q64, "0"="0", "1"="10", "2"="20", "3"="30", "4"="40", "5"="50",
                    "6"="60", "7"="70", "8"="80", "9"="90", "10"="100")
head(wave1$Q64)
## [1] "100" "10"  "90"  "100" "100" "100"
#[1] "100" "10"  "90"  "100" "100" "100"


#--NOTE---
#quality control item Q217 appears at column 84
#-  Procrastination - Q65 - Q70, column numbers: 87-92

wave1[, c(87:92)] <- sapply(wave1[, c(87:92)], as.numeric, is.na=NA)
#There were 42 warnings (use warnings() to see them)
wave1$procrastination<- rowSums(wave1[,c(87:92)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(87:92)]))

str(wave1$procrastination)
##  num [1:444] 10 22 6 23 21 21 24 18 24 14 ...
##  num [1:444] 10 22 6 23 21 21 24 18 24 14 ...
head(wave1$procrastination)
## [1] 10 22  6 23 21 21
## [1] 10 22  6 23 21 21

#Accommodation 
#Finances 
#-  Funding


wave1$Q75_1 <- recode(wave1$Q75_1, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_1)
## [1] "none" "none" "all"  NA     "none" "none"
#[1] "none" "none" "all"  NA     "none" "none"
wave1$Q75_2 <- recode(wave1$Q75_2, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_2)
## [1] "more than half" "none"           "none"           NA              
## [5] "none"           "none"
#[1] "more than half" "none"           "none"           NA               "none"           "none"  
wave1$Q75_3 <- recode(wave1$Q75_3, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_3)
## [1] "none" "all"  "none" NA     "all"  "none"
#[1] "none" "all"  "none" NA     "all"  "none"
wave1$Q75_4 <- recode(wave1$Q75_4, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_4)
## [1] "less than half" NA               "less than half" "less than half"
## [5] "less than half" "less than half"
#[1] "less than half" NA               "less than half" "less than half" "less than half" "less than half"

wave1$Q75_5 <- recode(wave1$Q75_5, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_5)
## [1] "half"           "less than half" "none"           "more than half"
## [5] "none"           "more than half"
#[1] "half"           "less than half" "none"           "more than half" "none"           "more than half"

wave1$Q75_6 <- recode(wave1$Q75_6, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_6)
## [1] "less than half" "none"           "less than half" NA              
## [5] "none"           "none"
#[1] "less than half" "none"           "less than half" NA               "none"           "none"  

wave1$Q75_7 <- recode(wave1$Q75_7, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_7)
## [1] "none" "none" "none" NA     "none" "none"
#[1] "none" "none" "none" NA     "none" "none"

wave1$Q75_8 <- recode(wave1$Q75_8, "1"="none", "2"="less than half", "3"="half", "4"="more than half", "5"="all")
head(wave1$Q75_8)
## [1] "none"           "none"           "none"           NA              
## [5] "none"           "less than half"
#[1] "none"           "none"           "none"           NA               "none"           "less than half"

wave1$Q76 <- recode(wave1$Q76, "1"="none", "2"="less £10k", "3"="£10k-£30k", "4"="£30k-£50k", "5"="£50k+")
head(wave1$Q76)
## [1] "less £10k" "none"      "£30k-£50k" "none"      "none"      "£10k-£30k"
#[1] "less £10k" "none"      "£30k-£50k" "none"      "none"      "£10k-£30k"

wave1$Q77 <- recode(wave1$Q77, "1"="not stressed", "2"="a little stressed", "3"="quite stressed", "4"="very stressed")
head(wave1$Q77)
## [1] "not stressed" NA             "not stressed" NA             NA            
## [6] "not stressed"
#[1] "not stressed" NA             "not stressed" NA             NA             "not stressed"

wave1$Q78 <- recode(wave1$Q78, "1"="yes", "2"="no")
head(wave1$Q78)
## [1] "no" "no" "no" "no" "no" "no"
#[1] "no" "no" "no" "no" "no" "no"

wave1$Q79 <- recode(wave1$Q79, "1"="yes", "2"="no")
head(wave1$Q79)
## [1] "no"  "no"  "yes" "no"  "no"  "no"
#[1] "no" "no" "yes" "no" "no" "no"


#-  Perceptions 


#GAD-7 - scoring, severity and above clinical cut-off - Q132_1 - Q132_7, columns 163 - 169

wave1$Q132_1[wave1$Q132_1 == ""] <- "NA"
wave1$Q132_2[wave1$Q132_2 == ""] <- "NA"
wave1$Q132_3[wave1$Q132_3 == ""] <- "NA"
wave1$Q132_4[wave1$Q132_4 == ""] <- "NA"
wave1$Q132_5[wave1$Q132_5 == ""] <- "NA"
wave1$Q132_6[wave1$Q132_6 == ""] <- "NA"
wave1$Q132_7[wave1$Q132_7 == ""] <- "NA"

wave1[, c(163:169)] <- sapply(wave1[, c(163:169)], as.numeric, is.na=NA)
#There were 40 warnings (use warnings() to see them)

wave1$GAD<- rowSums(wave1[,c(163:169)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(163:169)]))
str(wave1$GAD)
##  num [1:444] 0 16 18 13 2 3 7 3 10 5 ...
##  num [1:444] 0 16 18 13 2 3 7 3 10 5 ...

head(wave1$GAD)
## [1]  0 16 18 13  2  3
## [1]  0 16 18 13  2  3

wave1$GADseverity <- 'NA'
wave1$GADseverity[wave1$GAD==0] <- 'none'
wave1$GADseverity[wave1$GAD>0] <- 'none'
wave1$GADseverity[wave1$GAD>4] <- 'mild'
wave1$GADseverity[wave1$GAD>9] <- 'moderate'
wave1$GADseverity[wave1$GAD>14] <- 'severe'
head(wave1$GADseverity)  
## [1] "none"     "severe"   "severe"   "moderate" "none"     "none"
## [1] "none"     "severe"   "severe"   "moderate" "none"     "none"

wave1$GADseverity <- as.factor(wave1$GADseverity)
str(wave1$GADseverity)
##  Factor w/ 5 levels "mild","moderate",..: 4 5 5 2 4 4 1 4 2 1 ...
# Factor w/ 5 levels "mild","moderate",..: 4 5 5 2 4 4 1 4 2 1 ...

#GAD above clinical cut-off (=8)

wave1$GADclinical <- 'NA'
wave1$GADclinical[wave1$GAD==0] <- 'non-clinical'
wave1$GADclinical[wave1$GAD<8] <- 'non-clinical'
wave1$GADclinical[wave1$GAD>7] <- 'clinical'

head(wave1$GADclinical)
## [1] "non-clinical" "clinical"     "clinical"     "clinical"     "non-clinical"
## [6] "non-clinical"
## [1] "non-clinical" "clinical"     "clinical"     "clinical"     "non-clinical"
## [6] "non-clinical"

wave1$GADclinical <- as.factor(wave1$GADclinical)
str(wave1$GADclinical)
##  Factor w/ 3 levels "clinical","NA",..: 3 1 1 1 3 3 3 3 1 3 ...
##  Factor w/ 3 levels "clinical","NA",..: 3 1 1 1 3 3 3 3 1 3 ...

#PHQ-9 - questions Q133_1 to Q133_9, colnames = 170 - 178

wave1$Q133_1[wave1$Q133_1 == ""] <- "NA"
wave1$Q133_2[wave1$Q133_2 == ""] <- "NA"
wave1$Q133_3[wave1$Q133_3 == ""] <- "NA"
wave1$Q133_4[wave1$Q133_4 == ""] <- "NA"
wave1$Q133_5[wave1$Q133_5 == ""] <- "NA"
wave1$Q133_6[wave1$Q133_6 == ""] <- "NA"
wave1$Q133_7[wave1$Q133_7 == ""] <- "NA"
wave1$Q133_8[wave1$Q133_8 == ""] <- "NA"
wave1$Q133_9[wave1$Q133_9 == ""] <- "NA"

wave1[, c(170:178)] <- sapply(wave1[, c(170:178)], as.numeric, is.na=NA)

wave1$PHQ<- rowSums(wave1[,c(170:178)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(170:178)]))
str(wave1$PHQ)
##  num [1:444] 0 16 16 12 6 7 16 2 6 2 ...
##  num [1:444] 0 16 16 12 6 7 16 2 6 2 ...
head(wave1$PHQ)
## [1]  0 16 16 12  6  7
## [1]  0 16 16 12  6  7
wave1$PHQseverity <- 'NA'
wave1$PHQseverity[wave1$PHQ==0] <- 'none'
wave1$PHQseverity[wave1$PHQ>0] <- 'none'
wave1$PHQseverity[wave1$PHQ>4] <- 'mild'
wave1$PHQseverity[wave1$PHQ>9] <- 'moderate'
wave1$PHQseverity[wave1$PHQ>14] <- 'moderately severe'
wave1$PHQseverity[wave1$PHQ>19] <- 'severe'

head(wave1$PHQseverity) 
## [1] "none"              "moderately severe" "moderately severe"
## [4] "moderate"          "mild"              "mild"
## [1] "none"              "moderately severe" "moderately severe"
## [4] "moderate"          "mild"              "mild"

wave1$PHQseverity <- as.factor(wave1$PHQseverity)
str(wave1$PHQseverity)
##  Factor w/ 6 levels "mild","moderate",..: 5 3 3 2 1 1 3 5 1 5 ...
##  Factor w/ 6 levels "mild","moderate",..: 5 3 3 2 1 1 3 5 1 5 ...

#PHQ above clinical cut-off (=10)

wave1$PHQclinical <- 'NA'
wave1$PHQclinical[wave1$PHQ==0] <- 'non-clinical'
wave1$PHQclinical[wave1$PHQ<10] <- 'non-clinical'
wave1$PHQclinical[wave1$PHQ>=10] <- 'clinical'

head(wave1$PHQclinical)
## [1] "non-clinical" "clinical"     "clinical"     "clinical"     "non-clinical"
## [6] "non-clinical"
## [1] "non-clinical" "clinical"     "clinical"     "clinical"     "non-clinical"
## [6] "non-clinical"
wave1$PHQclinical <- as.factor(wave1$PHQclinical)
str(wave1$PHQclinical)
##  Factor w/ 3 levels "clinical","NA",..: 3 1 1 1 3 3 1 3 3 3 ...
##  Factor w/ 3 levels "clinical","NA",..: 3 1 1 1 3 3 1 3 3 3 ...

#SPIN Q136, Q137, Q138  - NOTE: recoded to 0-4 in the real thing. Columns 181 - 183

wave1[, c(179:181)] <- sapply(wave1[, c(179:181)], as.numeric, is.na=NA)

wave1$social.anxiety = rowSums(wave1[,c(179:181)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(179:181)]))

str(wave1$social.anxiety)
##  num [1:444] 2 5 7 7 10 2 8 2 5 0 ...
# num [1:444] 2 5 7 7 10 2 8 2 5 0 ...
head(wave1$social.anxiety)
## [1]  2  5  7  7 10  2
#[1]  2  5  7  7 10  2

#Brief Self-Control Scale - questions 2, 3, 4, 5, 7, 9, 10, 12 & 13 need to be reverse-scored
#question numbers:  Q140_1 - Q140_13
#column numbers: 182 - 194

wave1$Q140_2 <- recode(wave1$Q140_2, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_2)
## [1] "2" "1" "3" "2" "2" "2"
#[1] "2" "1" "3" "2" "2" "2"

wave1$Q140_3 <- recode(wave1$Q140_3, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_3)
## [1] "5" "2" "5" "2" "4" "2"
#[1] "5" "2" "5" "2" "4" "2"

wave1$Q140_4 <- recode(wave1$Q140_4, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_4)
## [1] "5" "3" "3" "4" "5" "3"
#[1] "5" "3" "3" "4" "5" "3"

wave1$Q140_5 <- recode(wave1$Q140_5, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_5)
## [1] "5" "5" "3" "4" "4" "4"
#[1] "5" "5" "3" "4" "4" "4"

wave1$Q140_7 <- recode(wave1$Q140_7, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_7)
## [1] "4" "1" "4" "1" "1" "2"
#[1] "4" "1" "4" "1" "1" "2"

wave1$Q140_9 <- recode(wave1$Q140_9, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_9)
## [1] "2" "2" "2" "2" "2" "2"
#[1] "2" "2" "2" "2" "2" "2"

wave1$Q140_10 <- recode(wave1$Q140_10, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_10)
## [1] "4" "4" "5" "2" "3" "3"
#[1] "4" "4" "5" "2" "3" "3"

wave1$Q140_12 <- recode(wave1$Q140_12, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_12)
## [1] "1" "4" "2" "2" "2" "2"
#[1] "1" "4" "2" "2" "2" "2"

wave1$Q140_13 <- recode(wave1$Q140_13, "1"="5", "2"="4", "3"="3", "4"="2", "5"="1")
head(wave1$Q140_13)
## [1] "5" "2" "4" "3" "4" "4"
#[1] "5" "2" "4" "3" "4" "4"

wave1[, c(182:194)] <- sapply(wave1[, c(182:194)], as.numeric, is.na=NA)

wave1$self.control = rowSums(wave1[,c(184:194)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(184:194)]))

str(wave1$self.control)
##  num [1:444] 42 32 35 32 36 30 24 30 39 41 ...
##  num [1:444] 42 32 35 32 36 30 24 30 39 41 ...

#AUDIT - unsure re. cut-off point. Work out total of AUDIT-5, plus the added item re. binge-drinking that we included
#Q141 - Q146
#columns 196 - 202
#----------------------NOTE: Quality control item Q220 is column number 200

wave1$Q141[wave1$Q141 == ""] <- "NA"
wave1$Q142[wave1$Q142 == ""] <- "NA"
wave1$Q143[wave1$Q143 == ""] <- "NA"
wave1$Q144[wave1$Q144 == ""] <- "NA"
wave1$Q145[wave1$Q145 == ""] <- "NA"
wave1$Q146[wave1$Q146 == ""] <- "NA"

wave1[, c(196:202)] <- sapply(wave1[, c(196:202)], as.numeric, is.na=NA)

#total - excluding item Q143 because not in the original AUDIT-5
#excluding column 200 because it is a quality control item
wave1$AUDIT = rowSums(wave1[,c(196, 197, 199, 201, 202)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(196, 197, 199, 201, 202)]))

head(wave1$AUDIT)
## [1] 1 0 4 3 5 5
#[1] 1 0 4 3 5 5

#according to Kim et al (2013) cut-offs are: >2 = problem drinking, >6 = possible alcohol use disorder, >10 = possible alcohol dependence

wave1$alcohol.screening <- 'NA'
wave1$alcohol.screening[wave1$AUDIT==0] <- 'negative'
wave1$alcohol.screening[wave1$AUDIT > 0] <- 'negative'
wave1$alcohol.screening[wave1$AUDIT > 2] <- 'problem drinking'
wave1$alcohol.screening[wave1$AUDIT > 6] <- 'alcohol use disorder'
wave1$alcohol.screening[wave1$AUDIT > 10] <- 'alcohol dependence'

head(wave1$alcohol.screening)
## [1] "negative"         "negative"         "problem drinking" "problem drinking"
## [5] "problem drinking" "problem drinking"
#[1] "negative"         "negative"         "problem drinking" "problem drinking" "problem drinking"
#[6] "problem drinking"

#CUDIT - screener = Q148, rest = Q149-Q155; columns 204 - 210

wave1$Q148[wave1$Q148 == ""] <- "NA"
wave1$Q149[wave1$Q149 == ""] <- "NA"
wave1$Q150[wave1$Q150 == ""] <- "NA"
wave1$Q151[wave1$Q151 == ""] <- "NA"
wave1$Q152[wave1$Q152 == ""] <- "NA"
wave1$Q153[wave1$Q153 == ""] <- "NA"
wave1$Q154[wave1$Q154 == ""] <- "NA"
wave1$Q155[wave1$Q155 == ""] <- "NA"

wave1[, c(204:210)] <- sapply(wave1[, c(204:210)], as.numeric, is.na=NA)

wave1$CUDIT = rowSums(wave1[,c(204:210)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(204:210)]))

head(wave1$CUDIT)
## [1]  0 NA NA  2 NA NA
#[1]  0 NA NA  2 NA NA

#cut-off: 8 or more = hazardous cannabis use, 12 or more indicate possible cannabis use disorder

wave1$cannabis.screening <- 'NA'
wave1$cannabis.screening[wave1$CUDIT==0] <- 'negative'
wave1$cannabis.screening[wave1$CUDIT > 0] <- 'negative'
wave1$cannabis.screening[wave1$CUDIT > 7] <- 'hazardous'
wave1$cannabis.screening[wave1$CUDIT > 11] <- 'cannabis use disorder'

head(wave1$cannabis.screening)
## [1] "negative" "NA"       "NA"       "negative" "NA"       "NA"
#[1] "negative" "NA"       "NA"       "negative" "NA"       "NA"


#Substance use Q156 - Q157_7

wave1$Q156 <- recode(wave1$Q156, "1"="yes", "2"="no")
head(wave1$Q156)
## [1] "no"  "no"  "yes" "no"  "no"  "no"
#[1] "no"  "no"  "yes" "no"  "no"  "no"

wave1$Q157_1 <- recode(wave1$Q157_1, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_1)
## [1] NA          NA          "10+ times" NA          NA          NA
#[1] NA          NA          "10+ times" NA          NA          NA     

wave1$Q157_2 <- recode(wave1$Q157_2, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_2)
## [1] NA   NA   "no" NA   NA   NA
#[1] NA   NA   "no" NA   NA   NA  

wave1$Q157_3 <- recode(wave1$Q157_3, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_3)
## [1] NA   NA   "no" NA   NA   NA
#[1] NA   NA   "no" NA   NA   NA  

wave1$Q157_4 <- recode(wave1$Q157_4, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_4)
## [1] NA     NA     "once" NA     NA     NA
#[1] NA     NA     "once" NA     NA     NA

wave1$Q157_5 <- recode(wave1$Q157_5, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_5)
## [1] NA   NA   "no" NA   NA   NA
#[1] NA   NA   "no" NA   NA   NA  

wave1$Q157_6 <- recode(wave1$Q157_6, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_6)
## [1] NA   NA   "no" NA   NA   NA
#[1] NA   NA   "no" NA   NA   NA  

wave1$Q157_7 <- recode(wave1$Q157_7, "0"="no", "1"="once", "2"="2-5 times", "3"="6-10 times", "4"="10+ times")
head(wave1$Q157_7)
## [1] NA   NA   "no" NA   NA   NA
#[1] NA   NA   "no" NA   NA   NA  
#Stress - Q159 - Q162, columns: 219 - 222

wave1[, c(219:222)] <- sapply(wave1[, c(219:222)], as.numeric, is.na=NA)

wave1$perceived.stress = rowSums(wave1[,c(219:222)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(219:222)]))

str(wave1$perceived.stress)
##  num [1:444] 1 11 9 9 5 5 12 4 10 1 ...
# num [1:444] 1 11 9 9 5 5 12 4 10 1 ...
head(wave1$perceived.stress)
## [1]  1 11  9  9  5  5
#[1]  1 11  9  9  5  5

#Wellbeing Q163 - Q176, columns 223 - 237
#NOTE: Q221 is a quality control item and is column number 230

wave1[, c(223:229, 231:237)][wave1[, c(223:229, 231:237)] == ""] <- "NA"

wave1[, c(223:229, 231:237)] <- sapply(wave1[, c(223:229, 231:237)], as.numeric, is.na=NA)

wave1$wellbeing<- rowSums(wave1[,c(223:229, 231:237)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(223:229, 231:237)]))

str(wave1$wellbeing)
##  num [1:444] 67 35 48 34 52 56 44 52 48 56 ...
# num [1:444] 67 35 48 34 52 56 44 52 48 56 ...
head(wave1$wellbeing)
## [1] 67 35 48 34 52 56
#[1] 67 35 48 34 52 56

#Sleep
#-  Sleep Hygiene Q178 - Q188. (238:248)

wave1$Q178 <- recode(wave1$Q178, "1"="00:00", "2"="00:30", "3"="01:00", "4"="01:30", "5"="02:00", 
                     "6"="02:30", "7"="03:00", "8"="03:30", "9"="04:00", "10"="04:30", 
                     "11"="05:00", "12"="05:30", "13"="06:00", "14"="06:30", "15"="07:00", 
                     "16"="07:30", "17"="08:00", "18"="08:30", "19"="09:00", "20"="09:30",
                     "21"="10:00", "22"="10:30", "23"="11:00", "24"="11:30", "25"="12:00",
                     "26"="12:30", "27"="13:00", "28"="13:30", "29"="14:00", "30"="14:30",
                     "31"="15:00", "32"="15:30", "33"="16:00", "34"="16:30", "35"="17:00",
                     "36"="17:30", "37"="18:00", "38"="18:30", "39"="19:00", "40"="19:30", 
                     "41"="20:00", "42"="20:30", "43"="21:00", "44"="21:30", "45"="22:00", 
                     "46"="22:30", "47"="23:00", "48"="23:30")
head(wave1$Q178)
## [1] "09:00" "08:30" "06:00" "09:30" "09:30" "08:30"
#[1] "09:00" "08:30" "06:00" "09:30" "09:30" "08:30"
wave1$Q179 <- recode(wave1$Q179, "1"="00:00", "2"="00:30", "3"="01:00", "4"="01:30", "5"="02:00", 
                     "6"="02:30", "7"="03:00", "8"="03:30", "9"="04:00", "10"="04:30", 
                     "11"="05:00", "12"="05:30", "13"="06:00", "14"="06:30", "15"="07:00", 
                     "16"="07:30", "17"="08:00", "18"="08:30", "19"="09:00", "20"="09:30",
                     "21"="10:00", "22"="10:30", "23"="11:00", "24"="11:30", "25"="12:00",
                     "26"="12:30", "27"="13:00", "28"="13:30", "29"="14:00", "30"="14:30",
                     "31"="15:00", "32"="15:30", "33"="16:00", "34"="16:30", "35"="17:00",
                     "36"="17:30", "37"="18:00", "38"="18:30", "39"="19:00", "40"="19:30", 
                     "41"="20:00", "42"="20:30", "43"="21:00", "44"="21:30", "45"="22:00", 
                     "46"="22:30", "47"="23:00", "48"="23:30")
#Warning message:
#Unreplaced values treated as NA as `.x` is not compatible.
#Please specify replacements exhaustively or supply `.default`.

head(wave1$Q179)
## [1] "23:30" "23:30" "22:00" "03:00" "22:30" "23:30"
#[1] "23:30" "23:30" "22:00" "03:00" "22:30" "23:30"

wave1$Q180 <- recode(wave1$Q180, "1"="00:00", "2"="00:30", "3"="01:00", "4"="01:30", "5"="02:00", 
                     "6"="02:30", "7"="03:00", "8"="03:30", "9"="04:00", "10"="04:30", 
                     "11"="05:00", "12"="05:30", "13"="06:00", "14"="06:30", "15"="07:00", 
                     "16"="07:30", "17"="08:00", "18"="08:30", "19"="09:00", "20"="09:30",
                     "21"="10:00", "22"="10:30", "23"="11:00", "24"="11:30", "25"="12:00",
                     "26"="12:30", "27"="13:00", "28"="13:30", "29"="14:00", "30"="14:30",
                     "31"="15:00", "32"="15:30", "33"="16:00", "34"="16:30", "35"="17:00",
                     "36"="17:30", "37"="18:00", "38"="18:30", "39"="19:00", "40"="19:30", 
                     "41"="20:00", "42"="20:30", "43"="21:00", "44"="21:30", "45"="22:00", 
                     "46"="22:30", "47"="23:00", "48"="23:30")
head(wave1$Q180)
## [1] "10:00" "09:30" "08:00" "14:00" "09:30" "10:00"
#[1] "10:00" "09:30" "08:00" "14:00" "09:30" "10:00"

wave1$Q181 <- recode(wave1$Q181, "1"="00:00", "2"="00:30", "3"="01:00", "4"="01:30", "5"="02:00", 
                     "6"="02:30", "7"="03:00", "8"="03:30", "9"="04:00", "10"="04:30", 
                     "11"="05:00", "12"="05:30", "13"="06:00", "14"="06:30", "15"="07:00", 
                     "16"="07:30", "17"="08:00", "18"="08:30", "19"="09:00", "20"="09:30",
                     "21"="10:00", "22"="10:30", "23"="11:00", "24"="11:30", "25"="12:00",
                     "26"="12:30", "27"="13:00", "28"="13:30", "29"="14:00", "30"="14:30",
                     "31"="15:00", "32"="15:30", "33"="16:00", "34"="16:30", "35"="17:00",
                     "36"="17:30", "37"="18:00", "38"="18:30", "39"="19:00", "40"="19:30", 
                     "41"="20:00", "42"="20:30", "43"="21:00", "44"="21:30", "45"="22:00", 
                     "46"="22:30", "47"="23:00", "48"="23:30")
#Warning message:
#Unreplaced values treated as NA as `.x` is not compatible.
#Please specify replacements exhaustively or supply `.default`.
head(wave1$Q181)
## [1] "49"    "00:00" "23:00" "04:00" "22:30" "01:00"
#[1] NA      "00:00" "23:00" "04:00" "22:30" "01:00"

wave1$Q182 <- recode(wave1$Q182, "10"="10+")
#Warning message:
#Unreplaced values treated as NA as `.x` is not compatible.
#Please specify replacements exhaustively or supply `.default`.
head(wave1$Q182)
## [1] "0" "0" "0" "0" "2" "1"
#[1] "0" "0" "0" "0" "2" "1"

#-  Sleep condition indicator Q189 - Q196; columns 249 - 256 

wave1$Q189[wave1$Q189 == ""] <- "NA"
wave1$Q190[wave1$Q190 == ""] <- "NA"
wave1$Q191[wave1$Q191 == ""] <- "NA"
wave1$Q192[wave1$Q192 == ""] <- "NA"
wave1$Q193[wave1$Q193 == ""] <- "NA"
wave1$Q194[wave1$Q194 == ""] <- "NA"
wave1$Q195[wave1$Q195 == ""] <- "NA"
wave1$Q196[wave1$Q196 == ""] <- "NA"



wave1[, c(249:256)] <- sapply(wave1[, c(249:256)], as.numeric, is.na=NA)

wave1$SCI = rowSums(wave1[,c(249:256)], na.rm=TRUE)

wave1$SCI = rowSums(wave1[,c(249:256)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(251:258)]))

head(wave1$SCI)
## [1] 31  6 19 18 11 29
#[1] 31  6 19 18 11 29

#sleep condition indicators cut-off - probable insomnia disorder = score of less than or equal to 16

wave1$insomnia.screening <- 'NA'
wave1$insomnia.screening[wave1$SCI==0] <- 'negative'
wave1$insomnia.screening[wave1$SCI > 0] <- 'negative'
wave1$insomnia.screening[wave1$SCI < 17] <- 'positive'

head(wave1$insomnia.screening)
## [1] "negative" "positive" "negative" "negative" "positive" "negative"
#[1] "negative" "positive" "negative" "negative" "positive" "negative"

#Perfectionism Q197 - Q204, columns 257-264

wave1[, c(257:264)] <- sapply(wave1[, c(257:264)], as.numeric, is.na=NA)

wave1$perfectionism = rowSums(wave1[,c(257:264)], na.rm=TRUE)*NA^!rowSums(!is.na(wave1[,c(257:264)]))
str(wave1$perfectionism)
##  num [1:444] 23 20 40 31 33 17 33 28 30 40 ...
# num [1:444] 23 20 40 31 33 17 33 28 30 40 ...
head(wave1$perfectionism)
## [1] 23 20 40 31 33 17
#[1] 23 20 40 31 33 17

#custom items - Student Experience Questions
#subscale 1 = Academic - Q60, Q61, Q62 and Q63. Columns 81, 82, 83 and 85 (84 is a quality control item)

str(wave1$Q60)
##  chr [1:444] "5" "4" "5" "2" "5" "4" "4" "5" "4" "5" "5" "5" "5" "5" "4" ...
#num [1:444] 5 4 5 2 5 4 4 5 4 5

wave1[, c(81:83, 85)] <- sapply(wave1[, c(81:83, 85)], as.numeric, is.na=NA)

wave1$SE.academic<- rowSums(wave1[,c(81:83, 85)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(81:83, 85)]))
str(wave1$SE.academic)
##  num [1:444] 19 10 19 14 16 17 15 17 14 17 ...
# num [1:444] 19 10 19 14 16 17 15 17 14 17 ...
head(wave1$SE.academic)
## [1] 19 10 19 14 16 17
#[1] 19 10 19 14 16 17

#subscale 2 = finances - Q80, Q81, Q82. Columns 109, 110, 112 (111 is a quality control item)

wave1[, c(109, 110, 112)] <- sapply(wave1[, c(109, 110, 112)], as.numeric, is.na=NA)

wave1$SE.finances<- rowSums(wave1[,c(109, 110, 112)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(109, 110, 112)]))
str(wave1$SE.finances)
##  num [1:444] 15 10 7 12 13 11 9 10 9 7 ...
#num [1:444] 15 10 7 12 13 11 9 10 9 7 ...
head(wave1$SE.finances)
## [1] 15 10  7 12 13 11
#[1] 15 10  7 12 13 11

#subscale 3 = accomodation -    Q71, Q72, Q73, Q74. Columns 93:96

wave1[, c(93:96)] <- sapply(wave1[, c(93:96)], as.numeric, is.na=NA)

wave1$SE.accom<- rowSums(wave1[,c(93:96)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(95:98)]))
str(wave1$SE.accom)
##  num [1:444] 18 20 12 15 13 16 19 19 16 20 ...
#num [1:444] 18 20 12 15 13 16 19 19 16 20 ...
head(wave1$SE.accom)
## [1] 18 20 12 15 13 16
#[1] 18 20 12 15 13 16

#subscale 4 = friendship, Q42, Q43. Columns 58 & 59

wave1[, c(58, 59)] <- sapply(wave1[, c(58, 59)], as.numeric, is.na=NA)

wave1$SE.friends<- rowSums(wave1[,c(58, 59)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(58, 59)]))
str(wave1$SE.friends)
##  num [1:444] 8 5 5 4 4 7 6 6 4 10 ...
#num [1:444] 8 5 5 4 4 7 6 6 4 10 ...
head(wave1$SE.friends)
## [1] 8 5 5 4 4 7
#[1] 8 5 5 4 4 7

#subscale 5 = community, Q26 & Q44, columns 42 & 60

wave1[, c(42, 60)] <- sapply(wave1[, c(42, 60)], as.numeric, is.na=NA)

wave1$SE.community<- rowSums(wave1[,c(42, 60)], na.rm = TRUE)*NA^!rowSums(!is.na(wave1[,c(42, 60)]))
str(wave1$SE.community)
##  num [1:444] 10 5 5 6 7 4 10 5 4 5 ...
#num [1:444] 10 5 5 6 7 4 10 5 4 5 ...
head(wave1$SE.community)
## [1] 10  5  5  6  7  4
#[1] 10  5  5  6  7  4

Cut-off Participants Progress

After exploring the data we decided that people who completed less than 10% of the survey (second pick in the data distribution) would be excluded from the study.

#completors and non-completors

str(wave1$Progress)
##  chr [1:444] "100" "100" "100" "100" "100" "100" "100" "100" "100" "100" ...
# chr [1:444] "100" "100" "100" "100" "100" "100" "100" "100" "100" "100" "100" "100" "100" ...

wave1$Progress <- as.numeric(wave1$Progress)

hist(wave1$Progress)

describe(wave1$Progress)
# vars   n mean    sd median trimmed mad min max range  skew kurtosis   se
#X1    1 444 61.3 45.13    100   63.76   0   0 100   100 -0.37     -1.8 2.14

#coding completors and non-completors

wave1$Progress <- as.numeric(wave1$Progress)

wave1$cut.off <- 'pass'
wave1$cut.off[wave1$Progress < 10] <- 'fail'
head(wave1$cut.off)
## [1] "pass" "pass" "pass" "pass" "pass" "pass"
#[1] "pass" "pass" "pass" "pass" "pass" "pass"

table(wave1$cut.off)
## 
## fail pass 
##  144  300
#fail pass 
# 144  300

pf <- as.data.frame(table(wave1$cut.off))

knitr::kable(pf)
Var1 Freq
fail 144
pass 300

File for Pass

I created a new file called “pass” in which we filtered out participants who completed less than 10% of the questionnaire.

pass <- filter(wave1, cut.off == "pass")

Quality Check Items

I singled out participants’ scores on the four quality check items.

We decided to exclude participants who completed less than 3 out of 4 quality check items.

quality <- pass[, c(9,84,111,200,230)]

quality[, c(2:5)] <- sapply(quality[, c(2:5)], as.numeric, is.na=NA)
quality$tot <- rowSums(quality[2:5])

quality$exc <- ifelse(quality$tot == 28, "keep", "exclude")

table(quality$exc)
## 
## exclude    keep 
##      50     189
#exclude    keep 
#50     189 

quality$tot[is.na(quality$tot)]<- 1
quality$exc <- ifelse(quality$tot == 28, "keep", "exclude")

table(quality$exc)
## 
## exclude    keep 
##     111     189
# exclude    keep 
#    111     189 


quality$Q220[is.na(quality$Q220)]<- 0
quality$Q217[is.na(quality$Q217)]<- 0
quality$Q218[is.na(quality$Q218)]<- 0
quality$Q221[is.na(quality$Q221)]<- 0

table(quality$Q217) #35 NA; 8 within one; 20 completely wrong
## 
##   0   9  10  11  12 
##  35  20   3 237   5
table(quality$Q218) #44 NA; 24 within one; 2 completely wrong
## 
##   0   8   9  11  12 
##  44   1   1  24 230
table(quality$Q220) #56 NA; 1 within one; 
## 
##   0   1   2 
##  56   1 243
table(quality$Q221) #60 NA; 2 within one;
## 
##   0   3   4 
##  60 238   2
table(quality$Q217,quality$Q218)
##     
##        0   8   9  11  12
##   0   34   0   0   0   1
##   9    0   0   0   0  20
##   10   0   0   0   0   3
##   11  10   1   1  24 201
##   12   0   0   0   0   5
####  0   8   9  11  12
#0   34   0   0   0   1
#9    0   0   0   0  20
#10   0   0   0   0   3
#11  10   1   1  24 201
#12   0   0   0   0   5


table(quality$Q217,quality$Q220)
##     
##        0   1   2
##   0   34   0   1
##   9    2   0  18
##   10   0   0   3
##   11  20   0 217
##   12   0   1   4
###   0   1   2
#0   34   0   1
#9    2   0  18
#10   0   0   3
#11  20   0 217
#12   0   1   4

table(quality$Q217,quality$Q221)
##     
##        0   3   4
##   0   34   1   0
##   9    2  18   0
##   10   0   3   0
##   11  24 211   2
##   12   0   5   0
###   0   3   4
#0   34   1   0
#9    2  18   0
#10   0   3   0
#11  24 211   2
#12   0   5   0



quality$Q217n <- ifelse(quality$Q217 == 11, 1, 0)
quality$Q218n <- ifelse(quality$Q218 == 12, 1, 0)
quality$Q220n <- ifelse(quality$Q220 == 2, 1, 0)
quality$Q221n <- ifelse(quality$Q221 == 3, 1, 0)
quality$totyn <- rowSums(quality[8:11])

qt <- data.frame(table(quality$totyn)) 
# 2+ = 252
# 3+ = 242
# 4  = 188

knitr::kable(qt)
Var1 Freq
0 34
1 14
2 10
3 54
4 188
quality$Two_plus <- ifelse(quality$totyn>= 2, 1,0)
quality$Three_plus <- ifelse(quality$totyn>= 3, 1,0)
quality$Four <- ifelse(quality$totyn>= 4, 1,0)

table(quality$Two_plus) 
## 
##   0   1 
##  48 252
table(quality$Three_plus)
## 
##   0   1 
##  58 242
table(quality$Four)
## 
##   0   1 
## 112 188
quality <- quality[,c(1,13:15)]

pass <- right_join(pass, quality)
## Joining with `by = join_by(ResponseId)`
pass <- filter(pass, Three_plus==1) 

pass <- filter(pass, Three_plus==1) 

Creation of Scales’ Cut-Offs Divisions

I used the scales cut off to devide participants in clinical/non-clinical or high/low scores.

#Creating new divisions for scales without cut off and with cut off WORKS####

#Perfectionism

pass$perfectionism.screening <- 'NA'
pass$perfectionism.screening[pass$perfectionism >= 8] <- 'Low Perfectionism'
pass$perfectionism.screening[pass$perfectionism >= 16] <- 'Moderate Perfectionism'
pass$perfectionism.screening[pass$perfectionism >= 32] <- 'High Perfectionism'
table(pass$perfectionism.screening)
## 
##     High Perfectionism      Low Perfectionism Moderate Perfectionism 
##                     81                      6                    149 
##                     NA 
##                      6
#    High Perfectionism      Low Perfectionism Moderate Perfectionism                     NA 
#                   81                      6                    149                      6 
head(pass$perfectionism)
## [1] 23 20 40 31 33 17
#[1] 23 20 40 31 33 17
head(pass$perfectionism.screening)
## [1] "Moderate Perfectionism" "Moderate Perfectionism" "High Perfectionism"    
## [4] "Moderate Perfectionism" "High Perfectionism"     "Moderate Perfectionism"
#[1] "Moderate Perfectionism" "Moderate Perfectionism" "High Perfectionism"     "Moderate Perfectionism"
#[5] "High Perfectionism"     "Moderate Perfectionism"


#Social Anxiety (actual cut off)

head(pass$social.anxiety)
## [1]  2  5  7  7 10  2
#[1]  2  5  7  7 10  2
pass$socialanxiety.clin<- 'NA'
pass$socialanxiety.clin[pass$social.anxiety < 6] <- 'Non-clinical'
pass$socialanxiety.clin[pass$social.anxiety >= 6] <- 'Clinical'
head(pass$socialanxiety.clin)
## [1] "Non-clinical" "Non-clinical" "Clinical"     "Clinical"     "Clinical"    
## [6] "Non-clinical"
#[1] "Non-clinical" "Non-clinical" "Clinical"     "Clinical"     "Clinical"     "Non-clinical"
table(pass$socialanxiety.clin)
## 
##     Clinical Non-clinical 
##           88          154
#  Clinical Non-clinical 
#     88          154 

#CUTOFF 

pass$procrastination.screening<-'NA'
pass$procrastination.screening[pass$procrastination >= 6]<-'Low Procrastination'
pass$procrastination.screening[pass$procrastination >= 18]<-'Moderate Procrastination'
pass$procrastination.screening[pass$procrastination >= 24]<-'High Procrastination'

table(pass$procrastination.screening)
## 
##     High Procrastination      Low Procrastination Moderate Procrastination 
##                       48                      108                       85 
##                       NA 
##                        1
#    High Procrastination      Low Procrastination Moderate Procrastination                       NA 
#                      48                      108                       85                        1 

#UCLA loneliness scale(pre and post uni start)(cut-off = 6)

pass$uni.loneliness.screening <-'NA'
pass$uni.loneliness.screening[pass$uni.loneliness>= 6]<-'Lonely'
pass$uni.loneliness.screening[pass$uni.loneliness<6]<-'Not Lonely'


table(pass$uni.loneliness.screening)
## 
##     Lonely Not Lonely 
##        142        100
## 
##     Lonely Not Lonely 
##        142        100


pass$pre.loneliness.screening <-'NA'
pass$pre.loneliness.screening[pass$pre.loneliness<6]<-'Not Lonely'
pass$pre.loneliness.screening[pass$pre.loneliness>= 6]<-'Lonely'
table(pass$pre.loneliness.screening)
## 
##     Lonely Not Lonely 
##        131        111
## 
##     Lonely Not Lonely 
##        131        111


#Self control

pass$self.control.screening <-'NA'
pass$self.control.screening[pass$self.control >=13]<-'Not Controlled'
pass$self.control.screening[pass$self.control >= 26]<-'Slightly Controlled'
pass$self.control.screening[pass$self.control >= 39]<-'Moderately Controlled'
pass$self.control.screening[pass$self.control >= 52]<-'Controlled'


table(pass$self.control.screening)
## 
## Moderately Controlled        Not Controlled   Slightly Controlled 
##                    53                    19                   170
## 
## Moderately Controlled        Not Controlled   Slightly Controlled 
##                    53                    19                   170


#Social anxiety

pass$social.anxiety.screening <-'NA'
pass$social.anxiety.screening [pass$social.anxiety <6]<-'Not Anxious'
pass$social.anxiety.screening [pass$social.anxiety >= 6]<-'Anxious'

table(pass$social.anxiety.screening)
## 
##     Anxious Not Anxious 
##          88         154
prop.table(table(pass$social.anxiety.screening))
## 
##     Anxious Not Anxious 
##   0.3636364   0.6363636
## 
##     Anxious Not Anxious 
##          88         154

#AUDIT
table(pass$alcohol.screening)
## 
##   alcohol dependence alcohol use disorder             negative 
##                    9                   15                  113 
##     problem drinking 
##                  105
#alcohol dependence       alcohol use disorder             negative          problem drinking 
#9                                15                          113                  105 
prop.table(table(pass$alcohol.screening))
## 
##   alcohol dependence alcohol use disorder             negative 
##           0.03719008           0.06198347           0.46694215 
##     problem drinking 
##           0.43388430
#CUDIT
table(pass$cannabis.screening)
## 
## cannabis use disorder             hazardous                    NA 
##                     4                     9                   129 
##              negative 
##                   100
prop.table(table(pass$cannabis.screening))
## 
## cannabis use disorder             hazardous                    NA 
##            0.01652893            0.03719008            0.53305785 
##              negative 
##            0.41322314
#cannabis use disorder             hazardous                   NA              negative 
#                   4                     9                   129                   100 

#Perceived Stress

pass$perceived.stress.screening <-'NA'
pass$perceived.stress.screening[pass$perceived.stress >= 0]<-'Little to no perceived stress'
pass$perceived.stress.screening[pass$perceived.stress>= 4]<-'Slightly perceived stress'
pass$perceived.stress.screening[pass$perceived.stress>= 8]<-'moderate perceived stress'
pass$perceived.stress.screening[pass$perceived.stress>= 12]<-'Highly perceived stress'
table(pass$perceived.stress.screening)
## 
##       Highly perceived stress Little to no perceived stress 
##                            26                            24 
##     moderate perceived stress                            NA 
##                            89                             4 
##     Slightly perceived stress 
##                            99
#   Highly perceived stress Little to no perceived stress     moderate perceived stress 
#            26                            24                            89 
#    NA     Slightly perceived stress 
#     4                            99 

#wellbeing
describe(pass$wellbeing)
pass$wellbeing.screening <- ifelse(pass$wellbeing>=35, "above", "below")
table(pass$wellbeing.screening)
## 
## above below 
##   213    25
#above below 
# 213    25 
prop.table(table(pass$wellbeing.screening))
## 
##    above    below 
## 0.894958 0.105042
#insomnia
table(pass$insomnia.screening)
## 
##       NA negative positive 
##        6      179       57
prop.table(table(pass$insomnia.screening))
## 
##         NA   negative   positive 
## 0.02479339 0.73966942 0.23553719
# NA negative positive 
# 6      179       57 


#Sleep Hygiene (higher scores = lower sleep hygiene)

pass[, c(244:249)] <- sapply(pass[, c(244:249)], as.numeric, is.na=NA)
pass$sleephygiene<- rowSums(pass[,c(244:249)], na.rm = TRUE)*NA^!rowSums(!is.na(pass[,c(244:249)]))
table(pass$sleephygiene)
## 
##  3  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 21 22 
##  1  7  8  4 20 34 26 25 27 24 23 15 13  3  2  2  1  1
# 3  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 21 22 
# 1  7  8  4 20 34 26 25 27 24 23 15 13  3  2  2  1  




test <- t.test(pass$uni.loneliness, pass$pre.loneliness, paired = TRUE, conf.level = 0.95)

#       Paired t-test

#data:  pass$uni.loneliness and pass$pre.loneliness
#t = 2.3338, df = 241, p-value = 0.02043
#alternative hypothesis: true mean difference is not equal to 0
#95 percent confidence interval:
#  0.04896928 0.57912990
#sample estimates:
#  mean difference 
#    0.3140496 

effectsize::cohens_d(pass$uni.loneliness, pass$pre.loneliness, paired = TRUE)
#Cohen's d |       95% CI
#0.15      | [0.02, 0.28]


#People drinking and not drinking
pass$drinking <- "NA"
pass$drinking[pass$Q141 == 0] <- "Not drinking"
pass$drinking[pass$Q141 >= 1] <- "regular to problematic"

table(pass$drinking)
## 
##           Not drinking regular to problematic 
##                     41                    201
# Not drinking regular to problematic 
#    41                    201 

drinkingtable <- table(pass$drinking)
prop.table(drinkingtable)
## 
##           Not drinking regular to problematic 
##              0.1694215              0.8305785
#           Not drinking            regular to problematic 
##              0.1694215              0.8305785
#People consuming cannabis and not 

pass$smoking <- "NA"
pass$smoking[pass$Q149 == 0] <- "Not smoking cannabis"
pass$smoking[pass$Q141 == 1] <- "regular to problematic"

table(pass$smoking)
## 
##                     NA   Not smoking cannabis regular to problematic 
##                    141                     48                     53
#    NA   Not smoking cannabis regular to problematic 
#   141                     48                     53 

prop.table(table(pass$smoking))
## 
##                     NA   Not smoking cannabis regular to problematic 
##              0.5826446              0.1983471              0.2190083
## 
##                     NA   Not smoking cannabis regular to problematic 
##              0.5826446              0.1983471              0.2190083

##recoding insomnia####

pass$Q189 <- dplyr::recode(pass$Q189,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q190 <- dplyr::recode(pass$Q190,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q191 <- dplyr::recode(pass$Q191,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q192 <- dplyr::recode(pass$Q192,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q193 <- dplyr::recode(pass$Q193,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q194 <- dplyr::recode(pass$Q194,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q195 <- dplyr::recode(pass$Q195,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")
pass$Q196 <- dplyr::recode(pass$Q196,"0" = "4", "1" ="3", "2"="2", "3"="1", "4"="0")

pass$Q189 <- as.numeric(pass$Q189)
pass$Q190 <- as.numeric(pass$Q190)
pass$Q191 <- as.numeric(pass$Q191)
pass$Q192 <- as.numeric(pass$Q192)
pass$Q193 <- as.numeric(pass$Q193)
pass$Q194 <- as.numeric(pass$Q194)
pass$Q195 <- as.numeric(pass$Q195)
pass$Q196 <- as.numeric(pass$Q196)

pass$SCIn <- rowSums(pass[,c(251:258)], na.rm = TRUE)*NA^!rowSums(!is.na(pass[,c(251:258)]))

hist(pass$SCIn)

PC <- as.data.frame(colnames(pass))

knitr::kable(PC)
colnames(pass)
StartDate
EndDate
Status
IPAddress
Progress
Duration..in.seconds.
Finished
RecordedDate
ResponseId
RecipientLastName
RecipientFirstName
RecipientEmail
ExternalReference
LocationLatitude
LocationLongitude
DistributionChannel
UserLanguage
Q1
Q2
Q3
Q4
Q5
Q8
Q9
Q11
Q12
Q13
Q16
Q16_4_TEXT
Q17
Q18
Q18_4_TEXT
Q19
Q19_5_TEXT
Q20
Q21
Q22
Q23
Q23_5_TEXT
Q24
Q25
Q26
Q27
Q28
Q29
Q30
Q31
Q32
Q33
Q34
Q35
Q36
Q37
Q38
Q39
Q40
Q41
Q42
Q43
Q44
Q45
Q46
Q47
Q48
Q49_1
Q49_2
Q50
Q51
Q53
Q54_1
Q54_2
Q55
Q55_0_TEXT
Q56_1
Q56_2
Q57
Q58_1
Q58_2
Q59_1
Q59_2
Q60
Q61
Q62
Q217
Q63
Q64
Q65
Q66
Q67
Q68
Q69
Q70
Q71
Q72
Q73
Q74
Q75_1
Q75_2
Q75_3
Q75_4
Q75_5
Q75_6
Q75_7
Q75_8
Q76
Q77
Q78
Q79
Q80
Q81
Q218
Q82
Q83
Q84
Q85
Q86
Q87
Q88
Q89
Q90
Q91
Q92
Q93
Q94
Q95
Q96
Q97
Q98
Q99
Q100
Q101
Q102
Q103
Q104
Q105
Q106
Q107
Q108
Q109
Q110
Q111
Q112
Q113
Q114
Q115
Q116
Q117
Q118
Q119
Q120
Q121
Q122
Q123
Q124
Q125
Q126
Q127
Q128
Q129
Q130
Q131
Q131_4_TEXT
Q132_1
Q132_2
Q132_3
Q132_4
Q132_5
Q132_6
Q132_7
Q133_1
Q133_2
Q133_3
Q133_4
Q133_5
Q133_6
Q133_7
Q133_8
Q133_9
Q136
Q137
Q138
Q140_1
Q140_2
Q140_3
Q140_4
Q140_5
Q140_6
Q140_7
Q140_8
Q140_9
Q140_10
Q140_11
Q140_12
Q140_13
Q140_14
Q141
Q142
Q143
Q144
Q220
Q145
Q146
Q148
Q149
Q150
Q151
Q152
Q153
Q154
Q155
Q156
Q157_1
Q157_2
Q157_3
Q157_4
Q157_5
Q157_6
Q157_7
Q159
Q160
Q161
Q162
Q163
Q164
Q165
Q166
Q167
Q168
Q169
Q221
Q170
Q171
Q172
Q173
Q174
Q175
Q176
Q178
Q179
Q180
Q181
Q182
Q183
Q184
Q185
Q186
Q187
Q188
Q189
Q190
Q191
Q192
Q193
Q194
Q195
Q196
Q197
Q198
Q199
Q200
Q201
Q202
Q203
Q204
Q207
Q209
Q212
Q214
Q216
KCL
age18
UGPG
year
faculty
gender
transgender
orientation
ethnicity
student.status
age
disability
LD
accomodation
hm_KCL
hm_students
hm_friends
hm_family
hm_partner
hm_alone
hm_other
commute
employed
diag_depression
diag_mania
diag_GAD
diag_socialanx
diag_agoraphobia
diag_panic
diag_OCD
diag_anorexia
diag_bulimia
diag_binge
diag_schizophrenia
diag_psychosis
diag_PD
diag_autism
diag_ADHD
past.diagnosis
tx_medication
tx_therapy
tx_admission
tx_other
tx_none
placement
relocation
pre.loneliness
uni.loneliness
Q48B
relationship.status
relationship.months
v.mins
ipaq.v
m.mins
ipaq.m
w.mins
ipaq.walk
ipaq.sit
procrastination
GAD
GADseverity
GADclinical
PHQ
PHQseverity
PHQclinical
social.anxiety
self.control
AUDIT
alcohol.screening
CUDIT
cannabis.screening
perceived.stress
wellbeing
SCI
insomnia.screening
perfectionism
SE.academic
SE.finances
SE.accom
SE.friends
SE.community
cut.off
Two_plus
Three_plus
Four
perfectionism.screening
socialanxiety.clin
procrastination.screening
uni.loneliness.screening
pre.loneliness.screening
self.control.screening
social.anxiety.screening
perceived.stress.screening
wellbeing.screening
sleephygiene
drinking
smoking
SCIn

Demographics and Graphs

We decided to divide the demographics by grouping different minorities together.

Percentages of KCL demographics were provided by PowerBI.

# demographics Graph ####
describe(as.numeric(pass$age))
pass$disability[pass$disability== "disability"] <- "Disability"
pass$disability[pass$disability== "no disability"] <- "No Disability"
pass$disability[pass$disability== "prefer not to say"] <- "Unknown"
Table.disability <- as.data.frame(round(prop.table(table(pass$disability))*100,1))


pass$gender[pass$gender=="other"|pass$gender=="non-binary"] <- "Gender Minority"
pass$gender[pass$gender=="male"] <- "Men"
pass$gender[pass$gender=="female"] <- "Women"
Table.gender <- as.data.frame(round(prop.table(table(pass$gender))*100,1))


pass$nethnicity[pass$ethnicity=="Black"|
                  pass$ethnicity=="Asian"|pass$ethnicity=="Mixed"|
                  pass$ethnicity=="Other"] <- "Ethnic Minority"
pass$nethnicity[pass$ethnicity=="White"] <- "White"
pass$nethnicity[pass$ethnicity=="prefer not to say"] <- "Unknown"
Table.ethnicity <- as.data.frame(round(prop.table(table(pass$nethnicity))*100,1))

pass$orientation[pass$orientation== "heterosexual"] <- "Heterosexual"
pass$orientation[pass$orientation== "prefer not to say"] <- "Unknown"
pass$orientation[pass$orientation== "gay/lesbian"|pass$orientation== "mostly straight"|pass$orientation== "bisexual"|pass$orientation== "other"] <- "Sexual Minority"
Table.sexualorientation <- as.data.frame(round(prop.table(table(pass$orientation))*100,1))

pass$year[pass$year=="1"] <- "Year 1"
pass$year[pass$year=="2"] <- "Year 2"
pass$year[pass$year=="3"] <- "Year 3"
pass$year[pass$year=="4"| pass$year == "5"] <- "Year 4+"
Table.year <- as.data.frame(round(prop.table(table(pass$year))*100,1))

pass$UGPG[pass$UGPG=="UG"] <- "Undergraduate"
pass$UGPG[pass$UGPG=="PG"] <- "Postgraduate"
Table.UGPG <- as.data.frame(round(prop.table(table(pass$UGPG))*100,1))

pass$transgender[pass$transgender=="transgender"] <- "Transgender"
pass$transgender[pass$transgender=="cisgender"] <- "Cisgender"
Table.transgender <- as.data.frame(round(prop.table(table(pass$transgender))*100,1))

pass$student.status[pass$student.status=="home"] <- "Home"
pass$student.status[pass$student.status=="overseas"] <- "Overseas"
Table.studentstatus <- as.data.frame(round(prop.table(table(pass$student.status))*100,1))

colnames(Table.transgender)[1]<- "Sex"
colnames(Table.sexualorientation)[1]<- "Sexual Orientation"
colnames(Table.disability)[1] <- "Disability"
colnames(Table.year)[1] <- "Year"
colnames(Table.gender)[1] <- "Gender"
colnames(Table.ethnicity)[1] <- "Ethnicity"
colnames(Table.UGPG)[1] <- "Course Level"
colnames(Table.studentstatus)[1] <- "Student Status"

colnames(Table.transgender)[2]<- "%"
colnames(Table.sexualorientation)[2]<- "%"
colnames(Table.disability)[2] <- "%"
colnames(Table.year)[2] <- "%"
colnames(Table.gender)[2] <- "%"
colnames(Table.ethnicity)[2] <- "%"
colnames(Table.UGPG)[2] <- "%"
colnames(Table.studentstatus)[2] <- "%"


Table.transgender$Source <- "Uni-WiSE"
Table.sexualorientation$Source <- "Uni-WiSE"
Table.disability$Source <- "Uni-WiSE"
Table.year$Source <- "Uni-WiSE"
Table.gender$Source <- "Uni-WiSE"
Table.ethnicity$Source <- "Uni-WiSE"
Table.UGPG$Source <- "Uni-WiSE"
Table.studentstatus$Source <- "Uni-WiSE"

Kings.transgender <- data.frame(Sex = c("Transgender", "Cisgender"),
                                P = c(1.4, 98.6),
                                Source= c("KCL","KCL")
)

Kings.UGPG <- data.frame(Course = c("Undergraduate", "Postgraduate"),
                         P = c(61.7, 38.3),
                         Source= c("KCL","KCL")
)

Kings.year <- data.frame(Year = c("Year 1", "Year 2","Year 3","Year 4+"),
                         P = c(52.2, 21.1,18.6,8.1),
                         Source= c("KCL","KCL","KCL","KCL")
)

Kings.sexualorientation <- data.frame(`Sexual Orientation` = c("Heterosexual", "Sexual Minority","Unknown"),
                                      P = c(84, 7,9),
                                      Source= c("KCL","KCL","KCL")
)

Kings.gender <- data.frame(Gender = c("Men", "Women","Gender Minority"),
                           P = c(63, 37,1),
                           Source= c("KCL","KCL","KCL")
)

Kings.studentstatus <- data.frame(StudentStatus = c("Home", "EU","Overseas"),
                                  P = c(61.3, 19.9,18.8),
                                  Source= c("KCL","KCL","KCL")
)


Kings.ethnicity <- data.frame(Ethnicity = c("White", "Ethnic Minority", "Unknown"),
                              P = c(57, 41, 2),
                              Source= c("KCL","KCL","KCL")
)

Kings.disability <- data.frame(Disability = c("No Disability", "Disability", "Unknown"),
                               P = c(88.4,11.6, 0),
                               Source= c("KCL","KCL","KCL")
)

colnames(Kings.sexualorientation)[1]<- "Sexual Orientation"
colnames(Kings.studentstatus)[1]<- "Student Status"
colnames(Kings.UGPG)[1] <- "Course Level"
colnames(Kings.transgender)[2]<- "%"
colnames(Kings.sexualorientation)[2]<- "%"
colnames(Kings.disability)[2] <- "%"
colnames(Kings.year)[2] <- "%"
colnames(Kings.gender)[2] <- "%"
colnames(Kings.ethnicity)[2] <- "%"
colnames(Kings.UGPG)[2] <- "%"
colnames(Kings.studentstatus)[2] <- "%"

transgender <- rbind(Table.transgender,Kings.transgender)
ethnicity <- rbind(Table.ethnicity,Kings.ethnicity)
gender<- rbind(Table.gender,Kings.gender)
sexualorientation <- rbind(Table.sexualorientation,Kings.sexualorientation)
studentstatus<- rbind(Table.studentstatus,Kings.studentstatus)
disability <- rbind(Table.disability,Kings.disability)
UGPG<- rbind(Table.UGPG,Kings.UGPG)
year <- rbind(Table.year,Kings.year)

transgender$Sex <- factor(transgender$Sex, levels = c("Cisgender", "Transgender"))
UGPG$`Course Level` <- factor(UGPG$`Course Level`, levels = c("Undergraduate", "Postgraduate"))
studentstatus$`Student Status` <- factor(studentstatus$`Student Status`, levels = c("Overseas", "EU","Home"))

knitr::kable(transgender, "pipe")
Sex % Source
Cisgender 98.8 Uni-WiSE
Transgender 1.2 Uni-WiSE
Transgender 1.4 KCL
Cisgender 98.6 KCL
knitr::kable(ethnicity, "pipe")
Ethnicity % Source
Ethnic Minority 38.4 Uni-WiSE
Unknown 0.8 Uni-WiSE
White 60.7 Uni-WiSE
White 57.0 KCL
Ethnic Minority 41.0 KCL
Unknown 2.0 KCL
knitr::kable(gender, "pipe")
Gender % Source
Gender Minority 2.1 Uni-WiSE
Men 11.6 Uni-WiSE
Women 86.4 Uni-WiSE
Men 63.0 KCL
Women 37.0 KCL
Gender Minority 1.0 KCL
knitr::kable(sexualorientation, "pipe")
Sexual Orientation % Source
Heterosexual 70.7 Uni-WiSE
Sexual Minority 25.9 Uni-WiSE
Unknown 3.3 Uni-WiSE
Heterosexual 84.0 KCL
Sexual Minority 7.0 KCL
Unknown 9.0 KCL
knitr::kable(studentstatus, "pipe")
Student Status % Source
EU 21.5 Uni-WiSE
Home 51.2 Uni-WiSE
Overseas 27.3 Uni-WiSE
Home 61.3 KCL
EU 19.9 KCL
Overseas 18.8 KCL
knitr::kable(disability, "pipe")
Disability % Source
Disability 15.7 Uni-WiSE
No Disability 79.3 Uni-WiSE
Unknown 5.0 Uni-WiSE
No Disability 88.4 KCL
Disability 11.6 KCL
Unknown 0.0 KCL
knitr::kable(UGPG, "pipe")
Course Level % Source
Postgraduate 35.1 Uni-WiSE
Undergraduate 64.9 Uni-WiSE
Undergraduate 61.7 KCL
Postgraduate 38.3 KCL
knitr::kable(year, "pipe")
Year % Source
Year 1 59.5 Uni-WiSE
Year 2 15.7 Uni-WiSE
Year 3 16.1 Uni-WiSE
Year 4+ 8.7 Uni-WiSE
Year 1 52.2 KCL
Year 2 21.1 KCL
Year 3 18.6 KCL
Year 4+ 8.1 KCL
##Colour####
cared <- "#AD0505"
  cbblue <- "#005AB5" 
    cbyellow <- "#FFEC5A"
      cbazz <- "#648FFF"
        mycols<- c(cared,cbblue,cbyellow,cbazz)
        cvi_colours = list(
          cvi_purples = c("#381532", "#4b1b42", "#5d2252", "#702963",
                                   "#833074", "#953784", "#a83e95"),
                                   mycols= c("#AD0505", "#005AB5", "#FFEC5A", "#648FFF"))
        
        Chloe_palette = function(name, n, all_palettes = cvi_colours, type = c("discrete", "continuous")) {
          palette = all_palettes[[name]]
          if (missing(n)) {
            n = length(palette)
          }
          type = match.arg(type)
          out = switch(type,
                       continuous = grDevices::colorRampPalette(palette)(n),
                       discrete = palette[1:n]
          )
          structure(out, name = name, class = "palette")
        }
        
        a2<- studentstatus%>%
          ggplot(aes(x=Source, y=`%`, fill = `Student Status`)) + 
          geom_bar(stat="identity", colour  = "black", width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip()
        
        b2<- UGPG%>%
          ggplot(aes(x=Source, y=`%`, fill = `Course Level`)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
        
        c2<- disability%>%
          ggplot( aes(x=Source, y=`%`, fill = Disability)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
        
        d2<- transgender%>% 
          ggplot(aes(x=Source, y=`%`, fill = Sex)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
        
        e2<- sexualorientation%>%
          ggplot(aes(x=Source, y=`%`, fill = `Sexual Orientation`)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
        
        
        f2<- ethnicity%>%
          ggplot(aes(x=Source, y=`%`, fill = Ethnicity)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
        
        
        g2<- gender%>%
          ggplot(aes(x=Source, y=`%`, fill = Gender)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
        
        h2<- year%>%
          ggplot(aes(x=Source, y=`%`, fill = Year)) + 
          geom_bar(stat="identity", colour  = 'black', width = 0.5, position = "stack")+
          theme_classic()+ scale_fill_manual(values=Chloe_palette("mycols", type="discrete"))+
          theme(axis.line.y.left=element_line(colour ="white"),axis.title.y = element_blank(),
                axis.title.x = element_text(face = "bold", colour = "black") ,
                axis.text = element_text(face="bold", colour="black"), 
                axis.line.x.bottom=element_line(colour="white"), 
                axis.text.y = element_text(angle = 90, vjust = 0.5, hjust=0.6), 
                axis.ticks = element_blank(),axis.text.x=element_blank(),legend.title=element_blank(),
                legend.key.size = unit(0.5, 'cm'),legend.key.height = unit(0.5, 'cm'),
                legend.key.width = unit(0.5, 'cm'), legend.text = element_text(size=10), 
                legend.position = "bottom", legend.direction = "horizontal")+
          coord_flip() 
ggpubr::ggarrange(a2,b2,c2,d2,e2,f2,g2,h2)
Figure 1. Demographics of Uni-WiSE and KCL

Figure 1. Demographics of Uni-WiSE and KCL

#Creating table 1: Diagnoses Reported by participants

To look at how many participants reported a diagnosis, I created separate tables showing the number and percentages of participants divided by diagnosed and undiagnosed. The tables were merged by their common column

#Table 1 (Diagnosis) ####
Depression <- as.data.frame(table(pass$diag_depression))
Depression2<- as.data.frame(round(prop.table(table(pass$diag_depression)),3)*100)
colnames(Depression2) <- c("Var1", "%")
Depression$diagnosis <- c("Depression")
Depression <- right_join(Depression, Depression2)
## Joining with `by = join_by(Var1)`
Depression <- Depression[,c(3,2,4,1)]

Mania<- as.data.frame(table(pass$diag_mania))
Mania2<- as.data.frame(round(prop.table(table(pass$diag_mania)),3)*100)
colnames(Mania2) <- c("Var1", "%")
Mania$diagnosis <- c("Mania")
Mania <- right_join(Mania, Mania2)
## Joining with `by = join_by(Var1)`
Mania <- Mania[,c(3,2,4,1)]

Anxiety <- as.data.frame(table(pass$diag_GAD))
Anxiety2<- as.data.frame(round(prop.table(table(pass$diag_GAD)),3)*100)
colnames(Anxiety2) <- c("Var1", "%")
Anxiety$diagnosis <- c("Anxiety")
Anxiety <- right_join(Anxiety, Anxiety2)
## Joining with `by = join_by(Var1)`
Anxiety <- Anxiety[,c(3,2,4,1)]

socialanx <- as.data.frame(table(pass$diag_socialanx))
socialanx2 <- as.data.frame(round(prop.table(table(pass$diag_socialanx)),3)*100)
colnames(socialanx2) <- c("Var1", "%")
socialanx$diagnosis <- c("Social Anxiety")
socialanx <- right_join(socialanx, socialanx2)
## Joining with `by = join_by(Var1)`
socialanx <- socialanx[,c(3,2,4,1)]


Agoraphobia <- as.data.frame(table(pass$diag_agoraphobia))
Agoraphobia2<- as.data.frame(round(prop.table(table(pass$diag_agoraphobia)),3)*100)
colnames(Agoraphobia2) <- c("Var1", "%")
Agoraphobia$diagnosis <- c("Agoraphobia")
Agoraphobia <- right_join(Agoraphobia, Agoraphobia2)
## Joining with `by = join_by(Var1)`
Agoraphobia <- Agoraphobia[,c(3,2,4,1)]


PanicAttacks <- as.data.frame(table(pass$diag_panic))
PanicAttacks2<- as.data.frame(round(prop.table(table(pass$diag_panic)),3)*100)
colnames(PanicAttacks2) <- c("Var1", "%")
PanicAttacks$diagnosis <- c("Panic Attacks")
PanicAttacks <- right_join(PanicAttacks, PanicAttacks2)
## Joining with `by = join_by(Var1)`
PanicAttacks <- PanicAttacks[,c(3,2,4,1)]


OCD <- as.data.frame(table(pass$diag_OCD))
OCD2<- as.data.frame(round(prop.table(table(pass$diag_OCD)),3)*100)
colnames(OCD2) <- c("Var1", "%")
OCD$diagnosis <- c("Obsessive Compulsive Disorder")
OCD <- right_join(OCD, OCD2)
## Joining with `by = join_by(Var1)`
OCD <- OCD[,c(3,2,4,1)]

Anorexia<- as.data.frame(table(pass$diag_anorexia))
Anorexia2<- as.data.frame(round(prop.table(table(pass$diag_anorexia)),3)*100)
colnames(Anorexia2) <- c("Var1", "%")
Anorexia$diagnosis <- c("Anorexia")
Anorexia <- right_join(Anorexia, Anorexia2)
## Joining with `by = join_by(Var1)`
Anorexia <- Anorexia[,c(3,2,4,1)]

Bulimia<- as.data.frame(table(pass$diag_bulimia))
Bulimia2<- as.data.frame(round(prop.table(table(pass$diag_bulimia)),3)*100)
colnames(Bulimia2) <- c("Var1", "%")
Bulimia$diagnosis <- c("Bulimia")
Bulimia <- right_join(Bulimia, Bulimia2)
## Joining with `by = join_by(Var1)`
Bulimia <- Bulimia[,c(3,2,4,1)]

BingeEating<- as.data.frame(table(pass$diag_binge))
BingeEating2<- as.data.frame(round(prop.table(table(pass$diag_binge)),3)*100)
colnames(BingeEating2) <- c("Var1", "%")
BingeEating$diagnosis <- c("Binge Eating")
BingeEating <- right_join(BingeEating, BingeEating2)
## Joining with `by = join_by(Var1)`
BingeEating <- BingeEating[,c(3,2,4,1)]

Schizophrenia<- as.data.frame(table(pass$diag_schizophrenia))
Schizophrenia2<- as.data.frame(round(prop.table(table(pass$diag_schizophrenia)),3)*100)
colnames(Schizophrenia2) <- c("Var1", "%")
Schizophrenia$diagnosis <- c("Schizophrenia")
Schizophrenia <- right_join(Schizophrenia, Schizophrenia2)
## Joining with `by = join_by(Var1)`
Schizophrenia <- Schizophrenia[,c(3,2,4,1)]

Psychosis<- as.data.frame(table(pass$diag_psychosis))
Psychosis2<- as.data.frame(round(prop.table(table(pass$diag_psychosis)),3)*100)
colnames(Psychosis2) <- c("Var1", "%")
Psychosis$diagnosis <- c("Psychosis")
Psychosis <- right_join(Psychosis, Psychosis2)
## Joining with `by = join_by(Var1)`
Psychosis <- Psychosis[,c(3,2,4,1)]


PersonalityDisorder<- as.data.frame(table(pass$diag_psychosis))
PersonalityDisorder2<- as.data.frame(round(prop.table(table(pass$diag_psychosis)),3)*100)
colnames(PersonalityDisorder2) <- c("Var1", "%")
PersonalityDisorder$diagnosis <- c("Personality Disorder")
PersonalityDisorder <- right_join(PersonalityDisorder, PersonalityDisorder2)
## Joining with `by = join_by(Var1)`
PersonalityDisorder <- PersonalityDisorder[,c(3,2,4,1)]


ASD<- as.data.frame(table(pass$diag_autism))
ASD2<- as.data.frame(round(prop.table(table(pass$diag_autism)),3)*100)
colnames(ASD2) <- c("Var1", "%")
ASD$diagnosis <- c("Autsim Spectrum Disorder")
ASD<- right_join(ASD, ASD2)
## Joining with `by = join_by(Var1)`
ASD <- ASD[,c(3,2,4,1)]

ADHD<- as.data.frame(table(pass$diag_ADHD))
ADHD2<- as.data.frame(round(prop.table(table(pass$diag_ADHD)),3)*100)
colnames(ADHD2) <- c("Var1", "%")
ADHD$diagnosis <- c("Attentiobn Deficit Hyperactivity Disorder")
ADHD<- right_join(ADHD, ADHD2)
## Joining with `by = join_by(Var1)`
ADHD <- ADHD[,c(3,2,4,1)]

MH <- pass[,c(293:313)]
MH[, c(1:15)] <- sapply(MH[, c(1:15)], as.numeric, is.na=NA)
MH$total_disorders = rowSums(MH[,c(1:15)], na.rm=TRUE)*NA^!rowSums(!is.na(MH[,c(1:15)]))
MH$multiple_disorders <- ifelse(MH$total_disorders>1, "More than one", "one")
table(MH$multiple_disorders)
## 
## More than one           one 
##            87           155
MH <- filter(MH, MH$total_disorders > 0)
dim(MH)
## [1] 105  23
table1 <- rbind(Depression, Mania, Anxiety, socialanx, Agoraphobia, PanicAttacks, OCD, Anorexia,
                Bulimia, BingeEating, Schizophrenia, Psychosis, PersonalityDisorder, ASD, ADHD)
knitr::kable(table1, "pipe", caption = "Table 1: Reported Diagnosis")
Table 1: Reported Diagnosis
diagnosis Freq % Var1
Depression 161 66.5 FALSE
Depression 81 33.5 TRUE
Mania 232 95.9 FALSE
Mania 10 4.1 TRUE
Anxiety 165 68.2 FALSE
Anxiety 77 31.8 TRUE
Social Anxiety 214 88.4 FALSE
Social Anxiety 28 11.6 TRUE
Agoraphobia 235 97.1 FALSE
Agoraphobia 7 2.9 TRUE
Panic Attacks 202 83.5 FALSE
Panic Attacks 40 16.5 TRUE
Obsessive Compulsive Disorder 231 95.5 FALSE
Obsessive Compulsive Disorder 11 4.5 TRUE
Anorexia 228 94.2 FALSE
Anorexia 14 5.8 TRUE
Bulimia 234 96.7 FALSE
Bulimia 8 3.3 TRUE
Binge Eating 230 95.0 FALSE
Binge Eating 12 5.0 TRUE
Schizophrenia 242 100.0 FALSE
Psychosis 238 98.3 FALSE
Psychosis 4 1.7 TRUE
Personality Disorder 238 98.3 FALSE
Personality Disorder 4 1.7 TRUE
Autsim Spectrum Disorder 238 98.3 FALSE
Autsim Spectrum Disorder 4 1.7 TRUE
Attentiobn Deficit Hyperactivity Disorder 235 97.1 FALSE
Attentiobn Deficit Hyperactivity Disorder 7 2.9 TRUE

Creating Table 2: Modal indicators per scale

I saved the descriptive statistics for each scale and then selected the variables I n eeded the most. I then merged them all together.

#Table 2 (scales descriptives)####


GAD <- as.data.frame(psych::describe(pass$GAD))
GAD <- GAD[,c(1:4, 8:9, 13)]
GAD$vars[GAD$vars==1] <- "GAD"
PHQ <- as.data.frame(psych::describe(pass$PHQ))
PHQ<- PHQ[,c(1:4, 8:9, 13)]
PHQ$vars[PHQ$vars==1] <- "PHQ"
AUDIT <- as.data.frame(psych::describe(pass$AUDIT))
AUDIT <- AUDIT[,c(1:4, 8:9, 13)]
AUDIT$vars[AUDIT$vars==1] <- "AUDIT"
CUDIT <- as.data.frame(psych::describe(pass$CUDIT))
CUDIT<- CUDIT[,c(1:4, 8:9, 13)]
CUDIT$vars[CUDIT$vars==1] <- "CUDIT"
soc_anx <- as.data.frame(psych::describe(pass$social.anxiety))
soc_anx <- soc_anx[,c(1:4, 8:9, 13)]
soc_anx$vars[soc_anx$vars==1] <- "Social Anxiety"
SCI <- as.data.frame(psych::describe(pass$SCIn))
SCI <- SCI[,c(1:4, 8:9, 13)]
SCI$vars[SCI$vars==1] <- "Sleep Condition Indicator"
blank2 <-  data.frame(c("UCLA Loneliness Scale"),
                      c(""),
                      c(""), 
                      c(""), 
                      c(""),
                      c(""),
                      c(""))
colnames(blank2) <- c("vars","n", "mean", "sd", "min", "max", "se")
pre_loneliness <- as.data.frame(psych::describe(pass$pre.loneliness))
pre_loneliness <- pre_loneliness[,c(1:4, 8:9, 13)]
pre_loneliness$vars[pre_loneliness$vars==1] <- "Pre-University"
uni_loneliness <- as.data.frame(psych::describe(pass$uni.loneliness))
uni_loneliness <- uni_loneliness[,c(1:4, 8:9, 13)]
uni_loneliness$vars[uni_loneliness$vars==1] <- "Post-University"
blank1 <-  data.frame(c("Psychological Phenotypes"),
                      c(""), 
                      c(""),
                      c(""), 
                      c(""),
                      c(""),
                      c(""))
colnames(blank1) <- c("vars","n", "mean", "sd", "min", "max", "se")
wellbeing <- as.data.frame(psych::describe(pass$wellbeing))
wellbeing <- wellbeing[,c(1:4, 8:9, 13)]
wellbeing$vars[wellbeing$vars==1] <- "Warwick-Edinburgh Mental
Wellbeing Scale"
SC <- as.data.frame(psych::describe(pass$self.control))
SC <- SC[,c(1:4, 8:9, 13)]
SC$vars[SC$vars==1] <- "Brief Self-Control Scale"
perfectionism <- as.data.frame(psych::describe(pass$perfectionism))
perfectionism <- perfectionism[,c(1:4, 8:9, 13)]
perfectionism$vars[perfectionism$vars==1] <- "Brief Frost-Multidimensional Perfectionism Scale"
perceived_stress <- as.data.frame(psych::describe(pass$perceived.stress))
perceived_stress <- perceived_stress[,c(1:4, 8:9, 13)]
perceived_stress$vars[perceived_stress$vars==1] <- "Brief Perceived Stress Scale"
procrastination <- as.data.frame(psych::describe(pass$procrastination)) 
procrastination <- procrastination[,c(1:4, 8:9, 13)]
procrastination$vars[procrastination$vars==1] <- "Procrastination Scale"
SHL <- as.data.frame(psych::describe(pass$sleephygiene))
SHL <- SHL[,c(1:4, 8:9, 13)]
SHL$vars[SHL$vars==1] <- "Sleep Hygiene"

Table2 <- rbind(GAD,PHQ, AUDIT, CUDIT,soc_anx, SCI, SC, blank1, wellbeing, perfectionism, 
                blank2, pre_loneliness, uni_loneliness, perceived_stress, procrastination,
                SHL)
knitr::kable(Table2, "pipe", caption = "Table 2: Descriptive Statistics per Scale")
Table 2: Descriptive Statistics per Scale
vars n mean sd min max se
X1 GAD 242 7.53719008264463 5.62381026210287 0 21 0.361512215676313
X11 PHQ 242 8.9504132231405 6.4978148126052 0 27 0.417695356080685
X12 AUDIT 242 3.16528925619835 2.87599048768912 0 18 0.184875670588453
X13 CUDIT 113 2.69026548672566 3.79397936119494 0 19 0.356907555929275
X14 Social Anxiety 242 4.61570247933884 3.39835225546043 0 12 0.218454356790606
X15 Sleep Condition Indicator 236 16.2161016949153 6.74394253450925 4 34 0.438993267142508
X16 Brief Self-Control Scale 242 33.7066115702479 5.63192199141429 18 46 0.362033657385699
1 Psychological Phenotypes
X17 Warwick-Edinburgh Mental
Wellbeing Scale 238 45.7478991596639 9.36977678959508 18 70 0.607352420386818
X18 Brief Frost-Multidimensional Perfectionism Scale 236 28.2203389830508 6.30182086920758 12 40 0.410213598079182
11 UCLA Loneliness Scale
X19 Pre-University 242 5.58264462809917 1.93552410417284 3 9 0.124420201746421
X110 Post-University 242 5.89669421487603 1.88729905953783 3 9 0.121320178466017
X111 Brief Perceived Stress Scale 238 7.47899159663866 3.1950614304318 0 15 0.20710507161838
X112 Procrastination Scale 241 18.2489626556017 5.74132034780325 6 30 0.369830954632738
X113 Sleep Hygiene 236 11.3305084745763 3.15973358625345 3 22 0.205681137291935

Creating Table 3

#Table 3 ####
table(pass$diag_GAD, pass$diag_depression)
##        
##         FALSE TRUE
##   FALSE   143   22
##   TRUE     18   59
pass$DIAG <- ifelse(pass$diag_depression =="TRUE"|pass$diag_GAD =="TRUE", "Diagnosed","Not Diagnosed")
pass$DIAG[pass$diag_depression=="TRUE"&pass$diag_GAD=="FALSE"]<- "Depression"
pass$DIAG[pass$diag_depression=="FALSE"&pass$diag_GAD=="TRUE"]<- "Anxiety"
pass$DIAG[pass$diag_depression=="TRUE"&pass$diag_GAD=="TRUE"]<- "Both"

pass$CLIN <- ifelse(pass$GADclinical =="clinical"|pass$PHQclinical =="clinical", "Clinical","Non-Clinical")
pass$CLIN[pass$PHQclinical =="clinical"&pass$GADclinical =="non-clinical"]<- "Clinical Depression"
pass$CLIN[pass$GADclinical =="clinical"&pass$PHQclinical =="non-clinical"]<- "Clinical Anxiety"
pass$CLIN[pass$GADclinical =="clinical"&pass$PHQclinical =="clinical"]<- "Both Thresholds met"


table3 <-as.data.frame(table(pass$CLIN, pass$DIAG))

round(prop.table(table(pass$CLIN, pass$DIAG)), 3)*100
##                      
##                       Anxiety Both Depression Not Diagnosed
##   Both Thresholds met     2.9 12.8        3.7          10.3
##   Clinical Anxiety        1.2  2.9        0.4           7.9
##   Clinical Depression     0.0  2.5        0.8           4.1
##   Non-Clinical            3.3  6.2        4.1          36.8
knitr::kable(table3, "pipe", caption = "Table 3: Crosstable Diagnosis x Results of our Scales")
Table 3: Crosstable Diagnosis x Results of our Scales
Var1 Var2 Freq
Both Thresholds met Anxiety 7
Clinical Anxiety Anxiety 3
Clinical Depression Anxiety 0
Non-Clinical Anxiety 8
Both Thresholds met Both 31
Clinical Anxiety Both 7
Clinical Depression Both 6
Non-Clinical Both 15
Both Thresholds met Depression 9
Clinical Anxiety Depression 1
Clinical Depression Depression 2
Non-Clinical Depression 10
Both Thresholds met Not Diagnosed 25
Clinical Anxiety Not Diagnosed 19
Clinical Depression Not Diagnosed 10
Non-Clinical Not Diagnosed 89

Supplementary Tables

Same procedure as diagnosis

Supplementary Table 1

# supplementary table Therapy ####
mh <- pass[,c(116:162, 293:313)]
mh[, c(64:67)] <- sapply(mh[, c(64:67)], as.numeric, is.na=NA)

mh$total_medications <- rowSums(mh[,c(64:67)], na.rm=TRUE)*NA^!rowSums(!is.na(mh[,c(64:67)]))
mh$total_medications_f <- ifelse(mh$past.diagnosis=="TRUE", mh$total_medications, 0)
hist(mh$total_medications_f)

table(mh$total_medications_f)
## 
##   0   1   2   3   4 
## 143  30  56  12   1
prop.table(table(mh$total_medications_f))
## 
##           0           1           2           3           4 
## 0.590909091 0.123966942 0.231404959 0.049586777 0.004132231
table(mh$past.diagnosis)
## 
## FALSE  TRUE 
##   137   105
medication1 <- as.data.frame(table(mh$tx_medication))
medication1$Var1<- ifelse(medication1$Var1==0,"No Medication", "Medication")
medication1$treatment <- "medication"
medication_p <-  as.data.frame(round(prop.table(table(mh$tx_medication)),3)*100)
medication_p$Var1<- ifelse(medication_p$Var1==0,"No Medication", "Medication")
colnames(medication_p)<- c("Var1", "%")
medication <- right_join(medication1,medication_p)
## Joining with `by = join_by(Var1)`
medication<- medication[,c(3,1,2,4)]

therapy1 <- as.data.frame(table(mh$tx_therapy))
therapy1$Var1<- ifelse(therapy1$Var1==0,"No Therapy", "Therapy")
therapy1$treatment <- "Therapy"
therapy_p <-  as.data.frame(round(prop.table(table(mh$tx_therapy)),3)*100)
therapy_p$Var1<- ifelse(therapy_p$Var1==0,"No Therapy", "Therapy")
colnames(therapy_p)<- c("Var1", "%")
therapy <- right_join(therapy1,therapy_p)
## Joining with `by = join_by(Var1)`
therapy <- therapy[,c(3,1,2,4)]

admission1 <- as.data.frame(table(mh$tx_admission))
admission1$Var1<- ifelse(admission1$Var1==0,"No Admission", "Admission")
admission1$treatment <- "Admission"
admission_p <-  as.data.frame(round(prop.table(table(mh$tx_admission)),3)*100)
admission_p$Var1<- ifelse(admission_p$Var1==0,"No Admission", "Admission")
colnames(admission_p)<- c("Var1", "%")
admission <- right_join(admission1,admission_p)
## Joining with `by = join_by(Var1)`
admission <- admission[,c(3,1,2,4)]

other1 <- as.data.frame(table(mh$tx_other))
other1$Var1<- ifelse(other1$Var1==0,"No Other", "Other")
other1$treatment <- "Other"
other_p <-  as.data.frame(round(prop.table(table(mh$tx_other)),3)*100)
other_p$Var1<- ifelse(other_p$Var1==0, "No Other", "Other")
colnames(other_p)<- c("Var1", "%")
other <- right_join(other1, other_p)
## Joining with `by = join_by(Var1)`
other <- other[,c(3,1,2,4)]

none1 <- as.data.frame(table(mh$tx_none))
none1$Var1<- ifelse(none1$Var1==FALSE,"Undetermined", "None")
none1$treatment <- "None"
none_p <-  as.data.frame(round(prop.table(table(mh$tx_none)),3)*100)
none_p$Var1<- ifelse(none_p$Var1==FALSE,"Undetermined", "None")
colnames(none_p)<- c("Var1", "%")
none <- right_join(none1, none_p)
## Joining with `by = join_by(Var1)`
none <- none[,c(3,1,2,4)]

Supplementary_table1 <- rbind(medication, therapy, admission, other, none)

table(mh$total_medications>0)
## 
## FALSE  TRUE 
##   119   123
table(mh$total_medications>1)
## 
## FALSE  TRUE 
##   171    71
prop.table(table(mh$total_medications>1))
## 
##     FALSE      TRUE 
## 0.7066116 0.2933884
table(pass$Q131_4_TEXT)
## 
## Admission to an NHS psychiatric inpatient unit 
##                                              1 
##                                       Coaching 
##                                              1 
##                                      Group CBT 
##                                              1 
##                          Neurofeedback Therapy 
##                                              1 
##                              Online CBT course 
##                                              1 
##                             Spiritual healing  
##                                              1
knitr::kable(Supplementary_table1, "pipe", caption = "Supplementary Table 1: Therapy and Medication")
Supplementary Table 1: Therapy and Medication
treatment Var1 Freq %
medication No Medication 174 71.9
medication Medication 68 28.1
Therapy No Therapy 129 53.3
Therapy Therapy 113 46.7
Admission No Admission 222 91.7
Admission Admission 20 8.3
Other No Other 235 97.1
Other Other 7 2.9
None Undetermined 127 52.5
None None 115 47.5

Supplementary Table 2 & 3

#Supplementary table Age and time of onset #####

#Depression

mh$Q86 <- ifelse(mh$Q86 >=21, "21+", mh$Q86)
mh$Q86 <- ifelse(mh$Q86 <=15, "Below 15", mh$Q86)
depression_c <- as.data.frame(table(mh$Q86))
depression_c$condition <- "Depression"
depression_d <-  as.data.frame(round(prop.table(table(mh$Q86)),3)*100)
colnames(depression_d)<- c("Var1", "%")
depression_age <- right_join(depression_c, depression_d)
## Joining with `by = join_by(Var1)`
depression_age <- depression_age[,c(3,1,2,4)]

depression_e <- as.data.frame(table(mh$Q87))
depression_e$condition <- "Depression"
depression_f <-  as.data.frame(round(prop.table(table(mh$Q87)),3)*100)
colnames(depression_f)<- c("Var1", "%")
depression_time <- right_join(depression_e, depression_f)
## Joining with `by = join_by(Var1)`
depression_time <- depression_time[,c(3,1,2,4)]

depression_g <- as.data.frame(table(mh$Q88))
depression_g$condition <- "Depression"
depression_h <-  as.data.frame(round(prop.table(table(mh$Q88)),3)*100)
colnames(depression_h)<- c("Var1", "%")
depression_prognosis <- right_join(depression_g, depression_h)
## Joining with `by = join_by(Var1)`
depression_prognosis <- depression_prognosis[,c(3,1,2,4)]



#Mania
mh$Q89 <- ifelse(mh$Q89 >=21, "21+", mh$Q89)
mh$Q89 <- ifelse(mh$Q89 <=15, "Below 15", mh$Q89)
mania_c <- as.data.frame(table(mh$Q89))
mania_c$condition <- "Mania"
mania_d <-  as.data.frame(round(prop.table(table(mh$Q89)),3)*100)
colnames(mania_d)<- c("Var1", "%")
mania_age <- right_join(mania_c, mania_d)
## Joining with `by = join_by(Var1)`
mania_age <- mania_age[,c(3,1,2,4)]

mania_e <- as.data.frame(table(mh$Q90))
mania_e$condition <- "Mania"
mania_f <-  as.data.frame(round(prop.table(table(mh$Q90)),3)*100)
colnames(mania_f)<- c("Var1", "%")
mania_time <- right_join(mania_e, mania_f)
## Joining with `by = join_by(Var1)`
mania_time <- mania_time[,c(3,1,2,4)]

mania_g <- as.data.frame(table(mh$Q91))
mania_g$condition <- "Mania"
mania_h <-  as.data.frame(round(prop.table(table(mh$Q91)),3)*100)
colnames(mania_h)<- c("Var1", "%")
mania_prognosis <- right_join(mania_g, mania_h)
## Joining with `by = join_by(Var1)`
mania_prognosis <- mania_prognosis[,c(3,1,2,4)]


#Anxiety
mh$Q92 <- ifelse(mh$Q92 >=21, "21+", mh$Q92)
mh$Q92 <- ifelse(mh$Q92 <=15, "Below 15", mh$Q92)
anxiety_c <- as.data.frame(table(mh$Q92))
anxiety_c$condition <- "Anxiety"
anxiety_d <-  as.data.frame(round(prop.table(table(mh$Q92)),3)*100)
colnames(anxiety_d)<- c("Var1", "%")
anxiety_age <- right_join(anxiety_c, anxiety_d)
## Joining with `by = join_by(Var1)`
anxiety_age <- anxiety_age[,c(3,1,2,4)]

anxiety_e <- as.data.frame(table(mh$Q93))
anxiety_e$condition <- "Anxiety"
anxiety_f <-  as.data.frame(round(prop.table(table(mh$Q93)),3)*100)
colnames(anxiety_f)<- c("Var1", "%")
anxiety_time <- right_join(anxiety_e, anxiety_f)
## Joining with `by = join_by(Var1)`
anxiety_time <- anxiety_time[,c(3,1,2,4)]

anxiety_g <- as.data.frame(table(mh$Q94))
anxiety_g$condition <- "Anxiety"
anxiety_h <-  as.data.frame(round(prop.table(table(mh$Q94)),3)*100)
colnames(anxiety_h)<- c("Var1", "%")
anxiety_prognosis <- right_join(anxiety_g, anxiety_h)
## Joining with `by = join_by(Var1)`
anxiety_prognosis <- anxiety_prognosis[,c(3,1,2,4)]

#Social Anxiety

mh$Q95 <- ifelse(mh$Q95 >=21, "21+", mh$Q95)
mh$Q95 <- ifelse(mh$Q95 <=15, "Below 15", mh$Q95)
socialanxiety_c <- as.data.frame(table(mh$Q95))
socialanxiety_c$condition <- "Social Anxiety"
socialanxiety_d <-  as.data.frame(round(prop.table(table(mh$Q95)),3)*100)
colnames(socialanxiety_d)<- c("Var1", "%")
socialanxiety_age <- right_join(socialanxiety_c, socialanxiety_d)
## Joining with `by = join_by(Var1)`
socialanxiety_age <- socialanxiety_age[,c(3,1,2,4)]

socialanxiety_e <- as.data.frame(table(mh$Q96))
socialanxiety_e$condition <- "Social Anxiety"
socialanxiety_f <-  as.data.frame(round(prop.table(table(mh$Q96)),3)*100)
colnames(socialanxiety_f)<- c("Var1", "%")
socialanxiety_time <- right_join(socialanxiety_e, socialanxiety_f)
## Joining with `by = join_by(Var1)`
socialanxiety_time <- socialanxiety_time[,c(3,1,2,4)]

socialanxiety_g <- as.data.frame(table(mh$Q97))
socialanxiety_g$condition <- "Social Anxiety"
socialanxiety_h <-  as.data.frame(round(prop.table(table(mh$Q97)),3)*100)
colnames(socialanxiety_h)<- c("Var1", "%")
socialanxiety_prognosis <- right_join(socialanxiety_g, socialanxiety_h)
## Joining with `by = join_by(Var1)`
socialanxiety_prognosis <- socialanxiety_prognosis[,c(3,1,2,4)]


#Agoraphobia (no reports)
#mh$Q98 <- ifelse(mh$Q98 >=21, "21+", mh$Q98)
#mh$Q98 <- ifelse(mh$Q98 <=15, "Below 15", mh$Q98)
#agoraphobia_c <- as.data.frame(table(mh$Q98))
#agoraphobia_c$condition <- "Agoraphobia"
#agoraphobia_d <-  as.data.frame(round(prop.table(table(mh$Q98)),3)*100)
#colnames(agoraphobia_d)<- c("Var1", "%")
#agoraphobia_age <- right_join(agoraphobia_c, agoraphobia_d)
#agoraphobia_age <- agoraphobia_age[,c(3,1,2,4)]

#agoraphobia_e <- as.data.frame(table(mh$Q99))
#agoraphobia_e$condition <- "Agoraphobia"
#agoraphobia_f <-  as.data.frame(round(prop.table(table(mh$Q99)),3)*100)
#colnames(agoraphobia_f)<- c("Var1", "%")
#agoraphobia_time <- right_join(agoraphobia_e, agoraphobia_f)
#agoraphobia_time <- agoraphobia_time[,c(3,1,2,4)]

#agoraphobia_g <- as.data.frame(table(mh$Q100))
#agoraphobia_g$condition <- "Agoraphobia"
#agoraphobia_h <-  as.data.frame(round(prop.table(table(mh$Q100)),3)*100)
#colnames(agoraphobia_h)<- c("Var1", "%")
#agoraphobia_prognosis <- right_join(agoraphobia_g, agoraphobia_h)
#agoraphobia_prognosis <- agoraphobia_prognosis[,c(3,1,2,4)]


#Panic Attack
mh$Q101 <- ifelse(mh$Q101 >=21, "21+", mh$Q101)
mh$Q101 <- ifelse(mh$Q101 <=15, "Below 15", mh$Q101)
panicattacks_c <- as.data.frame(table(mh$Q101))
panicattacks_c$condition <- "Panic Attack Disorder"
panicattacks_d <-  as.data.frame(round(prop.table(table(mh$Q101)),3)*100)
colnames(panicattacks_d)<- c("Var1", "%")
panicattacks_age <- right_join(panicattacks_c, panicattacks_d)
## Joining with `by = join_by(Var1)`
panicattacks_age <- panicattacks_age[,c(3,1,2,4)]

panicattacks_e <- as.data.frame(table(mh$Q102))
panicattacks_e$condition <- "Panic Attack Disorder"
panicattacks_f <-  as.data.frame(round(prop.table(table(mh$Q102)),3)*100)
colnames(panicattacks_f)<- c("Var1", "%")
panicattacks_time <- right_join(panicattacks_e, panicattacks_f)
## Joining with `by = join_by(Var1)`
panicattacks_time <- panicattacks_time[,c(3,1,2,4)]

panicattacks_g <- as.data.frame(table(mh$Q103))
panicattacks_g$condition <- "Panic Attack Disorder"
panicattacks_h <-  as.data.frame(round(prop.table(table(mh$Q103)),3)*100)
colnames(panicattacks_h)<- c("Var1", "%")
panicattacks_prognosis <- right_join(panicattacks_g, panicattacks_h)
## Joining with `by = join_by(Var1)`
panicattacks_prognosis <- panicattacks_prognosis[,c(3,1,2,4)]


#OCD
mh$Q104 <- ifelse(mh$Q104 >=21, "21+", mh$Q104)
mh$Q104 <- ifelse(mh$Q104 <=15, "Below 15", mh$Q104)
OCD_c <- as.data.frame(table(mh$Q104))
OCD_c$condition <- "OCD"
OCD_d <-  as.data.frame(round(prop.table(table(mh$Q104)),3)*100)
colnames(OCD_d)<- c("Var1", "%")
OCD_age <- right_join(OCD_c, OCD_d)
## Joining with `by = join_by(Var1)`
OCD_age <- OCD_age[,c(3,1,2,4)]

OCD_e <- as.data.frame(table(mh$Q105))
OCD_e$condition <- "OCD"
OCD_f <-  as.data.frame(round(prop.table(table(mh$Q105)),3)*100)
colnames(OCD_f)<- c("Var1", "%")
OCD_time <- right_join(OCD_e, OCD_f)
## Joining with `by = join_by(Var1)`
OCD_time <- OCD_time[,c(3,1,2,4)]

OCD_g <- as.data.frame(table(mh$Q106))
OCD_g$condition <- "OCD"
OCD_h <-  as.data.frame(round(prop.table(table(mh$Q106)),3)*100)
colnames(OCD_h)<- c("Var1", "%")
OCD_prognosis <- right_join(OCD_g, OCD_h)
## Joining with `by = join_by(Var1)`
OCD_prognosis <- OCD_prognosis[,c(3,1,2,4)]


#Anorexia
mh$Q107 <- ifelse(mh$Q107 >=21, "21+", mh$Q107)
mh$Q107 <- ifelse(mh$Q107 <=15, "Below 15", mh$Q107)
anorexia_c <- as.data.frame(table(mh$Q107))
anorexia_c$condition <- "Anorexia"
anorexia_d <-  as.data.frame(round(prop.table(table(mh$Q107)),3)*100)
colnames(anorexia_d)<- c("Var1", "%")
anorexia_age <- right_join(anorexia_c, anorexia_d)
## Joining with `by = join_by(Var1)`
anorexia_age <- anorexia_age[,c(3,1,2,4)]

anorexia_e <- as.data.frame(table(mh$Q108))
anorexia_e$condition <- "Anorexia"
anorexia_f <-  as.data.frame(round(prop.table(table(mh$Q108)),3)*100)
colnames(anorexia_f)<- c("Var1", "%")
anorexia_time <- right_join(anorexia_e, anorexia_f)
## Joining with `by = join_by(Var1)`
anorexia_time <- anorexia_time[,c(3,1,2,4)]

anorexia_g <- as.data.frame(table(mh$Q109))
anorexia_g$condition <- "Anorexia"
anorexia_h <-  as.data.frame(round(prop.table(table(mh$Q109)),3)*100)
colnames(anorexia_h)<- c("Var1", "%")
anorexia_prognosis <- right_join(anorexia_g, anorexia_h)
## Joining with `by = join_by(Var1)`
anorexia_prognosis <- anorexia_prognosis[,c(3,1,2,4)]


#Bulimia

mh$Q110 <- ifelse(mh$Q110 >=21, "21+", mh$Q110)
mh$Q110 <- ifelse(mh$Q110 <=15, "Below 15", mh$Q110)
bulimia_c <- as.data.frame(table(mh$Q110))
bulimia_c$condition <- "Bulimia"
bulimia_d <-  as.data.frame(round(prop.table(table(mh$Q110)),3)*100)
colnames(bulimia_d)<- c("Var1", "%")
bulimia_age <- right_join(bulimia_c, bulimia_d)
## Joining with `by = join_by(Var1)`
bulimia_age <- bulimia_age[,c(3,1,2,4)]

bulimia_e <- as.data.frame(table(mh$Q111))
bulimia_e$condition <- "Bulimia"
bulimia_f <-  as.data.frame(round(prop.table(table(mh$Q111)),3)*100)
colnames(bulimia_f)<- c("Var1", "%")
bulimia_time <- right_join(bulimia_e, bulimia_f)
## Joining with `by = join_by(Var1)`
bulimia_time <- bulimia_time[,c(3,1,2,4)]

bulimia_g <- as.data.frame(table(mh$Q112))
bulimia_g$condition <- "Bulimia"
bulimia_h <-  as.data.frame(round(prop.table(table(mh$Q112)),3)*100)
colnames(bulimia_h)<- c("Var1", "%")
bulimia_prognosis <- right_join(bulimia_g, bulimia_h)
## Joining with `by = join_by(Var1)`
bulimia_prognosis <- bulimia_prognosis[,c(3,1,2,4)]

#Binge Eating
mh$Q113 <- ifelse(mh$Q113 >=21, "21+", mh$Q113)
mh$Q113 <- ifelse(mh$Q113 <=15, "Below 15", mh$Q113)
bingeeating_c <- as.data.frame(table(mh$Q113))
bingeeating_c$condition <- "Binge Eating"
bingeeating_d <-  as.data.frame(round(prop.table(table(mh$Q113)),3)*100)
colnames(bingeeating_d)<- c("Var1", "%")
bingeeating_age <- right_join(bingeeating_c, bingeeating_d)
## Joining with `by = join_by(Var1)`
bingeeating_age <- bingeeating_age[,c(3,1,2,4)]

bingeeating_e <- as.data.frame(table(mh$Q114))
bingeeating_e$condition <- "Binge Eating"
bingeeating_f <-  as.data.frame(round(prop.table(table(mh$Q114)),3)*100)
colnames(bingeeating_f)<- c("Var1", "%")
bingeeating_time <- right_join(bingeeating_e, bingeeating_f)
## Joining with `by = join_by(Var1)`
bingeeating_time <- bingeeating_time[,c(3,1,2,4)]

bingeeating_g <- as.data.frame(table(mh$Q115))
bingeeating_g$condition <- "Binge Eating"
bingeeating_h <-  as.data.frame(round(prop.table(table(mh$Q115)),3)*100)
colnames(bingeeating_h)<- c("Var1", "%")
bingeeating_prognosis <- right_join(bingeeating_g, bingeeating_h)
## Joining with `by = join_by(Var1)`
bingeeating_prognosis <- bingeeating_prognosis[,c(3,1,2,4)]

#schizophrenia

table(mh$Q116)
## < table of extent 0 >
#< table of extent 0 >
table(mh$Q117)
## < table of extent 0 >
#< table of extent 0 >
table(mh$Q118)
## < table of extent 0 >
#< table of extent 0 >

#psychosis
mh$Q119 <- ifelse(mh$Q113 >=21, "21+", mh$Q119)
mh$Q119 <- ifelse(mh$Q113 <=15, "Below 15", mh$Q119)
psychosis_c <- as.data.frame(table(mh$Q119))
psychosis_c$condition <- "Psychosis"
psychosis_d <-  as.data.frame(round(prop.table(table(mh$Q119)),3)*100)
colnames(psychosis_d)<- c("Var1", "%")
psychosis_age <- right_join(psychosis_c, psychosis_d)
## Joining with `by = join_by(Var1)`
psychosis_age <- psychosis_age[,c(3,1,2,4)]

psychosis_e <- as.data.frame(table(mh$Q120))
psychosis_e$condition <- "Psychosis"
psychosis_f <-  as.data.frame(round(prop.table(table(mh$Q120)),3)*100)
colnames(psychosis_f)<- c("Var1", "%")
psychosis_time <- right_join(psychosis_e, psychosis_f)
## Joining with `by = join_by(Var1)`
psychosis_time <- psychosis_time[,c(3,1,2,4)]

psychosis_g <- as.data.frame(table(mh$Q121))
psychosis_g$condition <- "Psychosis"
psychosis_h <-  as.data.frame(round(prop.table(table(mh$Q121)),3)*100)
colnames(psychosis_h)<- c("Var1", "%")
psychosis_prognosis <- right_join(psychosis_g, psychosis_h)
## Joining with `by = join_by(Var1)`
psychosis_prognosis <- psychosis_prognosis[,c(3,1,2,4)]

#Personality Disorder
mh$Q122 <- ifelse(mh$Q113 >=21, "21+", mh$Q122)
mh$Q122 <- ifelse(mh$Q113 <=15, "Below 15", mh$Q122)
personalitydisorder_c <- as.data.frame(table(mh$Q122))
personalitydisorder_c$condition <- "Personality Disorder"
personalitydisorder_d <-  as.data.frame(round(prop.table(table(mh$Q122)),3)*100)
colnames(personalitydisorder_d)<- c("Var1", "%")
personalitydisorder_age <- right_join(personalitydisorder_c, personalitydisorder_d)
## Joining with `by = join_by(Var1)`
personalitydisorder_age <- personalitydisorder_age[,c(3,1,2,4)]

personalitydisorder_e <- as.data.frame(table(mh$Q123))
personalitydisorder_e$condition <- "Personality Disorder"
personalitydisorder_f <-  as.data.frame(round(prop.table(table(mh$Q123)),3)*100)
colnames(personalitydisorder_f)<- c("Var1", "%")
personalitydisorder_time <- right_join(personalitydisorder_e, personalitydisorder_f)
## Joining with `by = join_by(Var1)`
personalitydisorder_time <- personalitydisorder_time[,c(3,1,2,4)]

personalitydisorder_g <- as.data.frame(table(mh$Q124))
personalitydisorder_g$condition <- "Personality Disorder"
personalitydisorder_h <-  as.data.frame(round(prop.table(table(mh$Q124)),3)*100)
colnames(personalitydisorder_h)<- c("Var1", "%")
personalitydisorder_prognosis <- right_join(personalitydisorder_g, personalitydisorder_h)
## Joining with `by = join_by(Var1)`
personalitydisorder_prognosis <- personalitydisorder_prognosis[,c(3,1,2,4)]

#ASD
mh$Q125 <- ifelse(mh$Q125 >=21, "21+", mh$Q125)
mh$Q125 <- ifelse(mh$Q125 <=15, "Below 15", mh$Q125)
ASD_c <- as.data.frame(table(mh$Q125))
ASD_c$condition <- "Autism Spectrum Disorder"
ASD_d <-  as.data.frame(round(prop.table(table(mh$Q125)),3)*100)
colnames(ASD_d)<- c("Var1", "%")
ASD_age <- right_join(ASD_c, ASD_d)
## Joining with `by = join_by(Var1)`
ASD_age <- ASD_age[,c(3,1,2,4)]

ASD_e <- as.data.frame(table(mh$Q126))
ASD_e$condition <- "Autism Spectrum Disorder"
ASD_f <-  as.data.frame(round(prop.table(table(mh$Q126)),3)*100)
colnames(ASD_f)<- c("Var1", "%")
ASD_time <- right_join(ASD_e, ASD_f)
## Joining with `by = join_by(Var1)`
ASD_time <- ASD_time[,c(3,1,2,4)]

ASD_g <- as.data.frame(table(mh$Q127))
ASD_g$condition <- "Autism Spectrum Disorder"
ASD_h <-  as.data.frame(round(prop.table(table(mh$Q127)),3)*100)
colnames(ASD_h)<- c("Var1", "%")
ASD_prognosis <- right_join(ASD_g, ASD_h)
## Joining with `by = join_by(Var1)`
ASD_prognosis <- ASD_prognosis[,c(3,1,2,4)]

#ADHD
mh$Q128 <- ifelse(mh$Q128 >=21, "21+", mh$Q128)
mh$Q128 <- ifelse(mh$Q128 <=15, "Below 15", mh$Q128)
ADHD_c <- as.data.frame(table(mh$Q128))
ADHD_c$condition <- "Attention Deficit Hyperactivity Disorder"
ADHD_d <-  as.data.frame(round(prop.table(table(mh$Q128)),3)*100)
colnames(ADHD_d)<- c("Var1", "%")
ADHD_age <- right_join(ADHD_c, ADHD_d)
## Joining with `by = join_by(Var1)`
ADHD_age <- ADHD_age[,c(3,1,2,4)]

ADHD_e <- as.data.frame(table(mh$Q129))
ADHD_e$condition <- "Attention Deficit Hyperactivity Disorder"
ADHD_f <-  as.data.frame(round(prop.table(table(mh$Q129)),3)*100)
colnames(ADHD_f)<- c("Var1", "%")
ADHD_time <- right_join(ADHD_e, ADHD_f)
## Joining with `by = join_by(Var1)`
ADHD_time <- ADHD_time[,c(3,1,2,4)]

ADHD_g <- as.data.frame(table(mh$Q130))
ADHD_g$condition <- "Attention Deficit Hyperactivity Disorder"
ADHD_h <-  as.data.frame(round(prop.table(table(mh$Q130)),3)*100)
colnames(ADHD_h)<- c("Var1", "%")
ADHD_prognosis <- right_join(ADHD_g, ADHD_h)
## Joining with `by = join_by(Var1)`
ADHD_prognosis <- ADHD_prognosis[,c(3,1,2,4)]

Supplementary_table_age <- rbind(depression_age, anxiety_age, socialanxiety_age, 
                                 anorexia_age, ASD_age, bingeeating_age, bulimia_age,
                                 mania_age, OCD_age, panicattacks_age, psychosis_age, 
                                 personalitydisorder_age, ADHD_age)
Supplementary_table_timeandprog <- rbind(depression_time, anxiety_time, socialanxiety_time, 
                                         anorexia_time, ASD_time, bingeeating_time, bulimia_time,
                                         mania_time, OCD_time, panicattacks_time,psychosis_time, 
                                         personalitydisorder_time, ADHD_time,depression_prognosis,
                                         anxiety_prognosis,  socialanxiety_prognosis,anorexia_prognosis, 
                                         ASD_prognosis, bingeeating_prognosis, bulimia_prognosis,
                                         mania_prognosis,OCD_prognosis, panicattacks_prognosis, 
                                         psychosis_prognosis, personalitydisorder_prognosis,
                                         ADHD_prognosis)

knitr::kable(Supplementary_table_age, "pipe", caption = "Supplementary Table 2: Age at the Time of Diagnosis")
Supplementary Table 2: Age at the Time of Diagnosis
condition Var1 Freq %
Depression 16 8 11.4
Depression 17 9 12.9
Depression 18 6 8.6
Depression 19 8 11.4
Depression 20 2 2.9
Depression 21+ 17 24.3
Depression Below 15 20 28.6
Anxiety 16 10 13.5
Anxiety 17 6 8.1
Anxiety 18 5 6.8
Anxiety 19 8 10.8
Anxiety 20 3 4.1
Anxiety 21+ 18 24.3
Anxiety Below 15 24 32.4
Social Anxiety 16 4 16.7
Social Anxiety 17 4 16.7
Social Anxiety 18 1 4.2
Social Anxiety 19 2 8.3
Social Anxiety 21+ 5 20.8
Social Anxiety Below 15 8 33.3
Anorexia 16 2 14.3
Anorexia 17 1 7.1
Anorexia 18 1 7.1
Anorexia 19 2 14.3
Anorexia 20 1 7.1
Anorexia 21+ 2 14.3
Anorexia Below 15 5 35.7
Autism Spectrum Disorder 16 1 25.0
Autism Spectrum Disorder Below 15 3 75.0
Binge Eating 16 1 8.3
Binge Eating 19 2 16.7
Binge Eating 20 1 8.3
Binge Eating 21+ 4 33.3
Binge Eating Below 15 4 33.3
Bulimia 16 2 25.0
Bulimia 19 1 12.5
Bulimia 21+ 2 25.0
Bulimia Below 15 3 37.5
Mania 19 1 16.7
Mania 20 1 16.7
Mania 21+ 2 33.3
Mania Below 15 2 33.3
OCD 16 1 10.0
OCD 17 1 10.0
OCD 19 2 20.0
OCD 20 1 10.0
OCD 21+ 1 10.0
OCD Below 15 4 40.0
Panic Attack Disorder 16 7 17.5
Panic Attack Disorder 17 6 15.0
Panic Attack Disorder 18 4 10.0
Panic Attack Disorder 19 5 12.5
Panic Attack Disorder 21+ 8 20.0
Panic Attack Disorder Below 15 10 25.0
Psychosis 21+ 8 100.0
Personality Disorder 21+ 8 100.0
Attention Deficit Hyperactivity Disorder 16 1 14.3
Attention Deficit Hyperactivity Disorder 19 2 28.6
Attention Deficit Hyperactivity Disorder 20 1 14.3
Attention Deficit Hyperactivity Disorder 21+ 2 28.6
Attention Deficit Hyperactivity Disorder Below 15 1 14.3
knitr::kable(Supplementary_table_timeandprog, "pipe", caption = "Supplementary Table 2: Time & Prognosis of Diagnosis")
Supplementary Table 2: Time & Prognosis of Diagnosis
condition Var1 Freq %
Depression after 23 32.9
Depression before 47 67.1
Anxiety after 20 27.0
Anxiety before 54 73.0
Social Anxiety after 5 20.0
Social Anxiety before 20 80.0
Anorexia after 3 23.1
Anorexia before 10 76.9
Autism Spectrum Disorder before 4 100.0
Binge Eating after 5 41.7
Binge Eating before 7 58.3
Bulimia after 2 25.0
Bulimia before 6 75.0
Mania after 3 50.0
Mania before 3 50.0
OCD after 1 9.1
OCD before 10 90.9
Panic Attack Disorder after 8 20.0
Panic Attack Disorder before 32 80.0
Psychosis before 4 100.0
Personality Disorder after 5 55.6
Personality Disorder before 4 44.4
Attention Deficit Hyperactivity Disorder after 3 42.9
Attention Deficit Hyperactivity Disorder before 4 57.1
Depression improved 14 20.0
Depression same 26 37.1
Depression worse 30 42.9
Anxiety improved 10 13.5
Anxiety same 27 36.5
Anxiety worse 37 50.0
Social Anxiety improved 4 16.0
Social Anxiety same 12 48.0
Social Anxiety worse 9 36.0
Anorexia improved 5 35.7
Anorexia same 8 57.1
Anorexia worse 1 7.1
Autism Spectrum Disorder improved 1 25.0
Autism Spectrum Disorder same 3 75.0
Binge Eating improved 2 16.7
Binge Eating same 3 25.0
Binge Eating worse 7 58.3
Bulimia improved 3 37.5
Bulimia same 3 37.5
Bulimia worse 2 25.0
Mania improved 1 16.7
Mania same 2 33.3
Mania worse 3 50.0
OCD improved 1 9.1
OCD same 8 72.7
OCD worse 2 18.2
Panic Attack Disorder improved 10 25.0
Panic Attack Disorder same 18 45.0
Panic Attack Disorder worse 12 30.0
Psychosis same 2 50.0
Psychosis worse 2 50.0
Personality Disorder improved 1 11.1
Personality Disorder same 6 66.7
Personality Disorder worse 2 22.2
Attention Deficit Hyperactivity Disorder improved 1 14.3
Attention Deficit Hyperactivity Disorder same 4 57.1
Attention Deficit Hyperactivity Disorder worse 2 28.6

Supplementary Table 4 & 5: Drugs

# cocaine
cocaine1 <- as.data.frame(table(pass$Q157_1))
cocaine1$drug<-"Cocaine"
cocaine2 <- as.data.frame(round(prop.table(table(pass$Q157_1)),3)*100)
colnames(cocaine2)<-c("Var1", "%")
cocaine <- right_join(cocaine1, cocaine2)
## Joining with `by = join_by(Var1)`
cocaine <- cocaine[,c(3,1,2,4)]

#crack
crack1 <- data.frame(table(pass$Q157_2))
crack1$drug<-"Crack"
crack2 <- as.data.frame(round(prop.table(table(pass$Q157_2)),3)*100)
colnames(crack2)<-c("Var1", "%")
crack <- right_join(crack1, crack2)
## Joining with `by = join_by(Var1)`
crack <- crack[,c(3,1,2,4)]

#MDMA
mdma1 <- as.data.frame(table(pass$Q157_3))
mdma1$drug<-"MDMA"
mdma2 <- as.data.frame(round(prop.table(table(pass$Q157_3)),3)*100)
colnames(mdma2)<-c("Var1", "%")
mdma <- right_join(mdma1, mdma2)
## Joining with `by = join_by(Var1)`
mdma <- mdma[,c(3,1,2,4)]

#inhalants
inhalants1 <- as.data.frame(table(pass$Q157_4) )
inhalants1$drug<-"Inhalants"
inhalants2 <- as.data.frame(round(prop.table(table(pass$Q157_4)),3)*100)
colnames(inhalants2)<-c("Var1", "%")
inhalants <- right_join(inhalants1, inhalants2)
## Joining with `by = join_by(Var1)`
inhalants <-inhalants[,c(3,1,2,4)]

#sedatives
sedatives1 <- as.data.frame(table(pass$Q157_5))
sedatives1$drug<-"Sedatives"
sedatives2 <- as.data.frame(round(prop.table(table(pass$Q157_5)),3)*100)
colnames(sedatives2)<-c("Var1", "%")
sedatives <- right_join(sedatives1, sedatives2)
## Joining with `by = join_by(Var1)`
sedatives <-sedatives[,c(3,1,2,4)]


# hallucinogens   
hallucinogens1 <- as.data.frame(table(pass$Q157_6))
hallucinogens1$drug<-"Hallucinogens"
hallucinogens2 <- as.data.frame(round(prop.table(table(pass$Q157_6)),3)*100)
colnames(hallucinogens2)<-c("Var1", "%")
hallucinogens <- right_join(hallucinogens1, hallucinogens2)
## Joining with `by = join_by(Var1)`
hallucinogens <-hallucinogens[,c(3,1,2,4)]

#oppioids
oppioids1<- as.data.frame(table(pass$Q157_7))
oppioids1$drug<-"Oppioids"
oppioids2 <- as.data.frame(round(prop.table(table(pass$Q157_7)),3)*100)
colnames(oppioids2)<-c("Var1", "%")
oppioids <- right_join(oppioids1, oppioids2)
## Joining with `by = join_by(Var1)`
oppioids <-oppioids[,c(3,1,2,4)]

Supplementary_table3 <- rbind(cocaine,crack, mdma, inhalants, sedatives, hallucinogens, oppioids)
table(pass$Q156)
## 
##  no yes 
## 191  48
#Supplementary table number of drugs used and number of times####


drugs <- filter(pass, pass$Q156 == 'yes')
dim(drugs)
## [1]  48 370
drugs <- drugs[,c(212:218)]
dim(drugs)
## [1] 48  7
#[1] 49  7

# creating binary yes or no variables and full scale variables for drug use
drugs$Cocaine <- "NA"
drugs$Cocaine <- ifelse(drugs$Q157_1=="no", 0, 1)
head(drugs$Cocaine)
## [1] 1 0 0 0 1 0
drugs$Cocaine <- as.numeric(drugs$Cocaine)
cocaine_a <- as.data.frame(table(drugs$Cocaine))
cocaine_b <- as.data.frame(round(prop.table(table(drugs$Cocaine)),3)*100)
colnames(cocaine_b)<-c("Var1", "%")
cocaine_N <- right_join(cocaine_a, cocaine_b)
## Joining with `by = join_by(Var1)`
cocaine_N$drug <- "Cocaine"
cocaine_N <-cocaine_N[,c(4,1,2,3)]


drugs$crack<- "NA"
drugs$crack <- ifelse(drugs$Q157_2=="no", 0, 1)
head(drugs$crack)
## [1] 0 0 0 0 0 0
drugs$crack<- as.numeric(drugs$crack)
crack_a <- as.data.frame(table(drugs$crack))
crack_b <- as.data.frame(round(prop.table(table(drugs$crack)),3)*100)
colnames(crack_b)<-c("Var1", "%")
crack_N <- right_join(crack_a, crack_b)
## Joining with `by = join_by(Var1)`
crack_N$drug <- "Crack"
crack_N <-crack_N[,c(4,1,2,3)]


drugs$mdma<- "NA"
drugs$mdma<- ifelse(drugs$Q157_3=="no", 0, 1)
head(drugs$mdma)
## [1] 0 0 1 0 1 0
drugs$mdma<- as.numeric(drugs$mdma)
mdma_a <- as.data.frame(table(drugs$mdma))
mdma_b <- as.data.frame(round(prop.table(table(drugs$mdma)),3)*100)
colnames(mdma_b)<-c("Var1", "%")
mdma_N <- right_join(mdma_a, mdma_b)
## Joining with `by = join_by(Var1)`
mdma_N$drug <- "MDMA"
mdma_N <-mdma_N[,c(4,1,2,3)]

drugs$inhalants<- "NA"
drugs$inhalants<- ifelse(drugs$Q157_4=="no", 0, 1)
head(drugs$inhalants)
## [1] 1 0 0 1 0 0
drugs$inhalants<- as.numeric(drugs$inhalants)
inhalants_a <- as.data.frame(table(drugs$inhalants))
inhalants_b <- as.data.frame(round(prop.table(table(drugs$inhalants)),3)*100)
colnames(inhalants_b)<-c("Var1", "%")
inhalants_N <- right_join(inhalants_a, inhalants_b)
## Joining with `by = join_by(Var1)`
inhalants_N$drug <- "Inhalants"
inhalants_N <-inhalants_N[,c(4,1,2,3)]

drugs$sedatives<- "NA"
drugs$sedatives<- ifelse(drugs$Q157_5=="no", 0, 1)
head(drugs$sedatives)
## [1] 0 0 0 0 0 1
drugs$sedatives<- as.numeric(drugs$sedatives)
sedatives_a <- as.data.frame(table(drugs$sedatives))
sedatives_b <- as.data.frame(round(prop.table(table(drugs$sedatives)),3)*100)
colnames(sedatives_b)<-c("Var1", "%")
sedatives_N <- right_join(sedatives_a, sedatives_b)
## Joining with `by = join_by(Var1)`
sedatives_N$drug <- "Sedatives"
sedatives_N <-sedatives_N[,c(4,1,2,3)]

drugs$hallucinogens<- "NA"
drugs$hallucinogens<- ifelse(drugs$Q157_6=="no", 0, 1)
head(drugs$hallucinogens)
## [1] 0 1 0 1 0 0
drugs$hallucinogens<- as.numeric(drugs$hallucinogens)
hallucinogens_a <- as.data.frame(table(drugs$hallucinogens))
hallucinogens_b <- as.data.frame(round(prop.table(table(drugs$hallucinogens)),3)*100)
colnames(hallucinogens_b)<-c("Var1", "%")
hallucinogens_N <- right_join(hallucinogens_a, hallucinogens_b)
## Joining with `by = join_by(Var1)`
hallucinogens_N$drug <- "Hallucinogens"
hallucinogens_N <-hallucinogens_N[,c(4,1,2,3)]

drugs$oppioids<- "NA"
drugs$oppioids<- ifelse(drugs$Q157_7=="no", 0, 1)
head(drugs$oppioids)
## [1] 0 0 0 0 0 0
drugs$oppioids<- as.numeric(drugs$oppioids)
oppioids_a <- as.data.frame(table(drugs$oppioids))
oppioids_b <- as.data.frame(round(prop.table(table(drugs$oppioids)),3)*100)
colnames(oppioids_b)<-c("Var1", "%")
oppioids_N <- right_join(oppioids_a, oppioids_b)
## Joining with `by = join_by(Var1)`
oppioids_N$drug <- "Oppioids"
oppioids_N <-oppioids_N[,c(4,1,2,3)]

drugs$total_drugs_used = rowSums(drugs[,c(8:14)], na.rm=TRUE)*NA^!rowSums(!is.na(drugs[,c(8:14)]))
total_a <- as.data.frame(table(drugs$total_drugs_used))
total_b <- as.data.frame(round(prop.table(table(drugs$total_drugs_used)),3)*100)
colnames(total_b)<-c("Var1", "%")
total_N <- right_join(total_a, total_b)
## Joining with `by = join_by(Var1)`
total_N$drug <- "Total"
total_N <-total_N[,c(4,1,2,3)]

Supplementary_table8 <-total_N[,c(4,1,2,3)]


Supplementary_table4 <- rbind(cocaine_N,crack_N, mdma_N, inhalants_N, sedatives_N, hallucinogens_N, oppioids_N)
Supplementary_table3$Var1 <- as.character(Supplementary_table3$Var1)
knitr::kable(Supplementary_table3, "pipe", caption = "Supplementary Table 4: Drug used and Amount of time")
Supplementary Table 4: Drug used and Amount of time
drug Var1 Freq %
Cocaine 10+ times 8 17.4
Cocaine 2-5 times 6 13.0
Cocaine 6-10 times 5 10.9
Cocaine no 19 41.3
Cocaine once 8 17.4
Crack 10+ times 1 2.2
Crack no 45 97.8
MDMA 10+ times 12 26.1
MDMA 2-5 times 6 13.0
MDMA 6-10 times 3 6.5
MDMA no 18 39.1
MDMA once 7 15.2
Inhalants 10+ times 1 2.2
Inhalants 2-5 times 6 13.0
Inhalants 6-10 times 1 2.2
Inhalants no 35 76.1
Inhalants once 3 6.5
Sedatives 10+ times 1 2.2
Sedatives 2-5 times 4 8.7
Sedatives 6-10 times 1 2.2
Sedatives no 35 76.1
Sedatives once 5 10.9
Hallucinogens 10+ times 3 6.7
Hallucinogens 2-5 times 13 28.9
Hallucinogens 6-10 times 2 4.4
Hallucinogens no 20 44.4
Hallucinogens once 7 15.6
Oppioids 10+ times 3 6.5
Oppioids 2-5 times 1 2.2
Oppioids no 40 87.0
Oppioids once 2 4.3
knitr::kable(Supplementary_table4, "pipe", caption = "Supplementary Table 5: Participant per drug")
Supplementary Table 5: Participant per drug
drug Var1 Freq %
Cocaine 0 19 41.3
Cocaine 1 27 58.7
Crack 0 45 97.8
Crack 1 1 2.2
MDMA 0 18 39.1
MDMA 1 28 60.9
Inhalants 0 35 76.1
Inhalants 1 11 23.9
Sedatives 0 35 76.1
Sedatives 1 11 23.9
Hallucinogens 0 20 44.4
Hallucinogens 1 25 55.6
Oppioids 0 40 87.0
Oppioids 1 6 13.0

Linear Regressions

Demographics x GAD or PHQ

z.score <- function(data) {
      new_data <- round(((data - mean(data, na.rm = TRUE))/sd(data, na.rm = TRUE)),1) 
       return(new_data)}

# Linear Regressions ####

pass$n_ethnicity<-pass$nethnicity
pass$n_ethnicity<-ifelse(pass$n_ethnicity=="Unknown", NA, pass$n_ethnicity)
pass$n_orientation<-pass$orientation
pass$n_orientation<-ifelse(pass$n_orientation=="Unknown", NA, pass$n_orientation)
pass$n_disability<-pass$disability
pass$n_disability<-ifelse(pass$n_disability=="Unknown", NA, pass$n_disability)

pass$UGPG<-as.factor(pass$UGPG)
pass$n_ethnicity<-as.factor(pass$n_ethnicity)
pass$n_gender<-as.factor(pass$gender)
pass$n_orientation<-as.factor(pass$n_orientation)
pass$student.status <-as.factor(pass$student.status)
pass$n_year<-as.factor(pass$year)
pass$n_disability <- as.factor(pass$n_disability)
pass$age <- as.numeric(pass$age)

contrasts(pass$n_disability) <- c(1,0)
contrasts(pass$UGPG) <- c(1,0)
contrasts(pass$n_ethnicity) <- c(1,0)
contrasts(pass$n_gender) <- cbind(c(1,0,0),
                                c(0,0,1))
contrasts(pass$n_year)
##         Year 2 Year 3 Year 4+
## Year 1       0      0       0
## Year 2       1      0       0
## Year 3       0      1       0
## Year 4+      0      0       1
contrasts(pass$n_orientation)
##                 Sexual Minority
## Heterosexual                  0
## Sexual Minority               1
contrasts(pass$student.status)<- cbind(c(1,0,0),
                                       c(0,0,1))

#Standardised scores
pass$unil_z <- z.score(pass$uni.loneliness)
pass$prel_z <- z.score(pass$pre.loneliness)
pass$PHQz <- z.score(pass$PHQ)
pass$GADz <- z.score(pass$GAD)
pass$SAz <- z.score(pass$social.anxiety)
pass$SCz <- z.score(pass$self.control)
pass$AUDITz <- z.score(pass$AUDIT)
pass$CUDITz <- z.score(pass$CUDIT)
pass$PSz <- z.score(pass$perceived.stress)
pass$wellbeingz <- z.score(pass$wellbeing)
pass$SCInz <- z.score(pass$SCIn)
pass$perfectionismz <- z.score(pass$perfectionism)
pass$SE_aca_z <- z.score(pass$SE.academic)
pass$SE_acc_z <- z.score(pass$SE.academic)
pass$SE_fr_z <- z.score(pass$SE.friends)
pass$SE_co_z <- z.score(pass$SE.community)

#Recoding finance
pass$Q80 <- dplyr::recode(pass$Q80, `5` = "1", `1` = "5", `2` ="4", `3` ="3", `4` ="2")
pass$Q80 <- as.numeric(pass$Q80)
pass$Q81 <- dplyr::recode(pass$Q81, `5` = "1", `1` = "5", `2` ="4", `3` ="3", `4` ="2")
pass$Q81 <- as.numeric(pass$Q81)
pass$Q82 <- dplyr::recode(pass$Q82, `5` = "1", `1` = "5", `2` ="4", `3` ="3", `4` ="2")
pass$Q82 <- as.numeric(pass$Q82)
pass$SE.finances_n<- rowSums(pass[,c(109, 110, 112)], na.rm = TRUE)*NA^!rowSums(!is.na(pass[,c(109, 110, 112)]))
str(pass$SE.finances_n)
##  num [1:242] 3 8 11 6 5 7 9 8 9 11 ...
head(pass$SE.finances_n)
## [1]  3  8 11  6  5  7
pass$SE_fi_z <- z.score(pass$SE.finances_n)

#GAD demo####
GAD_UGPG <- lm(GADz~ UGPG, pass)
GAD_n_ethnicity <- lm(GADz~ n_ethnicity, pass)
GAD_n_gender <- lm(GADz~ n_gender, pass)
GAD_n_orientation <- lm(GADz~ n_orientation, pass)
GAD_student.status<- lm(GADz~ student.status, pass)
GAD_n_year <- lm(GADz~ n_year, pass)
GAD_n_disability <- lm(GADz~ n_disability, pass)
GAD_Multiple <- lm(GADz~ n_disability+student.status+n_orientation+ 
                     n_ethnicity+n_gender+UGPG+n_year+age, pass)


summary(GAD_UGPG)
## 
## Call:
## lm(formula = GADz ~ UGPG, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3236 -0.8236 -0.2859  0.7453  2.4518 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.02357    0.07996   0.295    0.768
## UGPG1       -0.07533    0.13493  -0.558    0.577
## 
## Residual standard error: 1.002 on 240 degrees of freedom
## Multiple R-squared:  0.001297,   Adjusted R-squared:  -0.002864 
## F-statistic: 0.3117 on 1 and 240 DF,  p-value: 0.5771
summary(GAD_n_disability)
## 
## Call:
## lm(formula = GADz ~ n_disability, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6842 -0.7842 -0.3017  0.5557  2.5807 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -0.18073    0.06869  -2.631  0.00909 ** 
## n_disability1  0.86494    0.16899   5.118 6.55e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9518 on 228 degrees of freedom
##   (12 observations deleted due to missingness)
## Multiple R-squared:  0.1031, Adjusted R-squared:  0.09912 
## F-statistic:  26.2 on 1 and 228 DF,  p-value: 6.548e-07
summary(GAD_n_ethnicity)
## 
## Call:
## lm(formula = GADz ~ n_ethnicity, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3054 -0.8054 -0.2785  0.7946  2.4215 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.005442   0.083002   0.066    0.948
## n_ethnicity1 -0.026948   0.133338  -0.202    0.840
## 
## Residual standard error: 1.006 on 238 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  0.0001716,  Adjusted R-squared:  -0.004029 
## F-statistic: 0.04084 on 1 and 238 DF,  p-value: 0.84
summary(GAD_n_gender)
## 
## Call:
## lm(formula = GADz ~ n_gender, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3057 -0.8057 -0.3057  0.7443  2.3943 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  -0.1179     0.1896  -0.622    0.535
## n_gender1     0.3979     0.4870   0.817    0.415
## n_gender2     0.1236     0.2019   0.612    0.541
## 
## Residual standard error: 1.003 on 239 degrees of freedom
## Multiple R-squared:  0.003257,   Adjusted R-squared:  -0.005084 
## F-statistic: 0.3905 on 2 and 239 DF,  p-value: 0.6771
summary(GAD_n_orientation)
## 
## Call:
## lm(formula = GADz ~ n_orientation, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6532 -0.7580 -0.2532  0.6468  2.5373 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   -0.1373     0.0759  -1.809 0.071801 .  
## n_orientationSexual Minority   0.4905     0.1465   3.348 0.000951 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9867 on 229 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.04667,    Adjusted R-squared:  0.04251 
## F-statistic: 11.21 on 1 and 229 DF,  p-value: 0.0009509
summary(GAD_n_year)
## 
## Call:
## lm(formula = GADz ~ n_year, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4486 -0.7486 -0.2486  0.6514  2.3462 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    0.14861    0.08164   1.820 0.069980 .  
## n_yearYear 2  -0.22756    0.17868  -1.274 0.204057    
## n_yearYear 3  -0.29476    0.17686  -1.667 0.096890 .  
## n_yearYear 4+ -0.78671    0.22885  -3.438 0.000693 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9797 on 238 degrees of freedom
## Multiple R-squared:  0.05305,    Adjusted R-squared:  0.04112 
## F-statistic: 4.445 on 3 and 238 DF,  p-value: 0.004635
summary(GAD_student.status)
## 
## Call:
## lm(formula = GADz ~ student.status, data = pass)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.375 -0.797 -0.236  0.725  2.588 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)
## (Intercept)      0.07500    0.08975   0.836    0.404
## student.status1 -0.26346    0.16511  -1.596    0.112
## student.status2 -0.07803    0.15227  -0.512    0.609
## 
## Residual standard error: 0.9994 on 239 degrees of freedom
## Multiple R-squared:  0.01054,    Adjusted R-squared:  0.002261 
## F-statistic: 1.273 on 2 and 239 DF,  p-value: 0.2819
summary(GAD_Multiple)
## 
## Call:
## lm(formula = GADz ~ n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7708 -0.6369 -0.1206  0.5604  2.5533 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.41840    0.40974   1.021  0.30840    
## n_disability1                 0.77475    0.19056   4.066 6.84e-05 ***
## student.status1              -0.30813    0.17284  -1.783  0.07611 .  
## student.status2              -0.06096    0.17299  -0.352  0.72492    
## n_orientationSexual Minority  0.30993    0.15484   2.002  0.04666 *  
## n_ethnicity1                 -0.02670    0.15264  -0.175  0.86129    
## n_gender1                    -0.38011    0.53382  -0.712  0.47724    
## n_gender2                     0.11048    0.20205   0.547  0.58512    
## UGPG1                         0.03608    0.17264   0.209  0.83469    
## n_yearYear 2                 -0.34991    0.19688  -1.777  0.07701 .  
## n_yearYear 3                 -0.38663    0.18751  -2.062  0.04049 *  
## n_yearYear 4+                -0.70800    0.24613  -2.877  0.00445 ** 
## age                          -0.02223    0.01726  -1.288  0.19928    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9362 on 204 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2007, Adjusted R-squared:  0.1537 
## F-statistic: 4.269 on 12 and 204 DF,  p-value: 5.064e-06
PHQ_UGPG <- lm(PHQz~ UGPG, pass)
PHQ_n_ethnicity <- lm(PHQz~ n_ethnicity, pass)
PHQ_n_gender <- lm(PHQz~ n_gender, pass)
PHQ_n_orientation <- lm(PHQz~ n_orientation, pass)
PHQ_student.status<- lm(PHQz~ student.status, pass)
PHQ_n_year <- lm(PHQz~ n_year, pass)
PHQ_n_disability <- lm(PHQz~ n_disability, pass)
PHQ_Multiple <- lm(PHQz~ n_disability+student.status+
                     n_orientation+n_ethnicity+n_gender+
                     UGPG+n_year+age, pass)

summary(PHQ_UGPG)
## 
## Call:
## lm(formula = PHQz ~ UGPG, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4803 -0.8802 -0.1802  0.7482  2.9577 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.08025    0.07985   1.005   0.3159  
## UGPG1       -0.23790    0.13473  -1.766   0.0787 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.001 on 240 degrees of freedom
## Multiple R-squared:  0.01282,    Adjusted R-squared:  0.008711 
## F-statistic: 3.118 on 1 and 240 DF,  p-value: 0.07872
summary(PHQ_n_disability)
## 
## Call:
## lm(formula = PHQz ~ n_disability, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1395 -0.6896 -0.2395  0.5104  3.0104 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   -0.21042    0.06746  -3.119  0.00205 ** 
## n_disability1  0.94989    0.16597   5.723 3.27e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9348 on 228 degrees of freedom
##   (12 observations deleted due to missingness)
## Multiple R-squared:  0.1256, Adjusted R-squared:  0.1218 
## F-statistic: 32.76 on 1 and 228 DF,  p-value: 3.271e-08
summary(PHQ_n_ethnicity)
## 
## Call:
## lm(formula = PHQz ~ n_ethnicity, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4027 -0.8027 -0.2949  0.7973  2.8129 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)   0.002721   0.083403   0.033    0.974
## n_ethnicity1 -0.015624   0.133981  -0.117    0.907
## 
## Residual standard error: 1.011 on 238 degrees of freedom
##   (2 observations deleted due to missingness)
## Multiple R-squared:  5.714e-05,  Adjusted R-squared:  -0.004144 
## F-statistic: 0.0136 on 1 and 238 DF,  p-value: 0.9073
summary(PHQ_n_gender)
## 
## Call:
## lm(formula = PHQz ~ n_gender, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3857 -0.7856 -0.2857  0.7643  2.8144 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.07143    0.18925  -0.377   0.7062  
## n_gender1    0.91143    0.48618   1.875   0.0621 .
## n_gender2    0.05707    0.20152   0.283   0.7773  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.001 on 239 degrees of freedom
## Multiple R-squared:  0.01525,    Adjusted R-squared:  0.007008 
## F-statistic:  1.85 on 2 and 239 DF,  p-value: 0.1594
summary(PHQ_n_orientation)
## 
## Call:
## lm(formula = PHQz ~ n_orientation, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5532 -0.7657 -0.1657  0.6343  2.9343 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                  -0.13432    0.07627  -1.761  0.07955 . 
## n_orientationSexual Minority  0.48755    0.14721   3.312  0.00108 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9915 on 229 degrees of freedom
##   (11 observations deleted due to missingness)
## Multiple R-squared:  0.04571,    Adjusted R-squared:  0.04154 
## F-statistic: 10.97 on 1 and 229 DF,  p-value: 0.001077
summary(PHQ_n_year)
## 
## Call:
## lm(formula = PHQz ~ n_year, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4500 -0.8289 -0.2282  0.7500  2.8710 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    0.05000    0.08318   0.601   0.5484  
## n_yearYear 2  -0.12105    0.18204  -0.665   0.5067  
## n_yearYear 3   0.07821    0.18019   0.434   0.6647  
## n_yearYear 4+ -0.54048    0.23316  -2.318   0.0213 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9982 on 238 degrees of freedom
## Multiple R-squared:  0.02565,    Adjusted R-squared:  0.01337 
## F-statistic: 2.088 on 3 and 238 DF,  p-value: 0.1024
summary(PHQ_student.status)
## 
## Call:
## lm(formula = PHQz ~ student.status, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5193 -0.7332 -0.2379  0.7806  2.7212 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)  
## (Intercept)      0.11935    0.08977   1.330   0.1849  
## student.status1 -0.34051    0.16515  -2.062   0.0403 *
## student.status2 -0.18148    0.15231  -1.191   0.2346  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9996 on 239 degrees of freedom
## Multiple R-squared:  0.01874,    Adjusted R-squared:  0.01053 
## F-statistic: 2.283 on 2 and 239 DF,  p-value: 0.1042
summary(PHQ_Multiple)
## 
## Call:
## lm(formula = PHQz ~ n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5122 -0.6573 -0.2387  0.4786  2.6964 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.648290   0.407860   1.589   0.1135    
## n_disability1                 0.848812   0.189682   4.475 1.27e-05 ***
## student.status1              -0.331933   0.172043  -1.929   0.0551 .  
## student.status2              -0.147010   0.172193  -0.854   0.3942    
## n_orientationSexual Minority  0.218346   0.154131   1.417   0.1581    
## n_ethnicity1                  0.084540   0.151934   0.556   0.5785    
## n_gender1                     0.218510   0.531365   0.411   0.6813    
## n_gender2                     0.038327   0.201122   0.191   0.8491    
## UGPG1                        -0.030081   0.171851  -0.175   0.8612    
## n_yearYear 2                 -0.214235   0.195976  -1.093   0.2756    
## n_yearYear 3                 -0.006915   0.186653  -0.037   0.9705    
## n_yearYear 4+                -0.345010   0.244999  -1.408   0.1606    
## age                          -0.035135   0.017182  -2.045   0.0422 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9319 on 204 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.1982, Adjusted R-squared:  0.151 
## F-statistic: 4.202 on 12 and 204 DF,  p-value: 6.573e-06
car::Anova(PHQ_Multiple, type ="III")
### Assumption Check for demo #####

car::leveneTest(lm(GADz~ UGPG, pass)) #good
car::leveneTest(lm(GADz~ n_ethnicity, pass))#good
car::leveneTest(lm(GADz~ n_gender, pass))#good
car::leveneTest(lm(GADz~ n_orientation, pass))#good
car::leveneTest(lm(GADz~ student.status, pass))#good
car::leveneTest(lm(GADz~ n_year, pass))#good
car::leveneTest(lm(GADz~ n_disability, pass))#good
car::leveneTest(lm(PHQz~ UGPG, pass)) #good
car::leveneTest(lm(PHQz~ n_ethnicity, pass))#good
car::leveneTest(lm(PHQz~ n_gender, pass))#good
car::leveneTest(lm(PHQz~ n_orientation, pass))#good
car::leveneTest(lm(PHQz~ student.status, pass))#good
car::leveneTest(lm(PHQz~ n_year, pass))#good
car::leveneTest(lm(PHQz~ n_disability, pass))#good
DVIF <- car::vif(GAD_Multiple)
1/DVIF #GOOD
##                     GVIF        Df GVIF^(1/(2*Df))
## n_disability   0.7864075 1.0000000       0.8867962
## student.status 0.6505352 0.5000000       0.8980855
## n_orientation  0.8902581 1.0000000       0.9435349
## n_ethnicity    0.7339713 1.0000000       0.8567213
## n_gender       0.8217266 0.5000000       0.9520984
## UGPG           0.5821800 1.0000000       0.7630072
## n_year         0.7150111 0.3333333       0.9456247
## age            0.5619035 1.0000000       0.7496022
DVIF2 <- car::vif(PHQ_Multiple)
1/DVIF2 #GOOD
##                     GVIF        Df GVIF^(1/(2*Df))
## n_disability   0.7864075 1.0000000       0.8867962
## student.status 0.6505352 0.5000000       0.8980855
## n_orientation  0.8902581 1.0000000       0.9435349
## n_ethnicity    0.7339713 1.0000000       0.8567213
## n_gender       0.8217266 0.5000000       0.9520984
## UGPG           0.5821800 1.0000000       0.7630072
## n_year         0.7150111 0.3333333       0.9456247
## age            0.5619035 1.0000000       0.7496022
psych::describe(stats::resid(PHQ_UGPG))
psych::describe(stats::resid(PHQ_n_disability))
psych::describe(stats::resid(PHQ_n_ethnicity))
psych::describe(stats::resid(PHQ_n_orientation))
psych::describe(stats::resid(PHQ_n_gender))
psych::describe(stats::resid(PHQ_n_year))
## ALL SKEWED +
psych::describe(stats::resid(GAD_UGPG))
psych::describe(stats::resid(GAD_n_disability))
psych::describe(stats::resid(GAD_n_ethnicity))
psych::describe(stats::resid(GAD_n_orientation))
psych::describe(stats::resid(GAD_n_gender))
psych::describe(stats::resid(GAD_n_year))
## ALL SKEWED +

summary(stats::cooks.distance(GAD_UGPG))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.396e-05 8.808e-04 2.179e-03 4.297e-03 5.395e-03 3.607e-02
summary(stats::cooks.distance(GAD_n_disability))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.893e-05 5.106e-04 1.771e-03 4.540e-03 3.639e-03 4.510e-02
summary(stats::cooks.distance(GAD_n_ethnicity))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 3.044e-05 8.698e-04 2.209e-03 4.259e-03 5.194e-03 3.181e-02
summary(stats::cooks.distance(GAD_n_orientation))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 4.274e-06 5.881e-04 2.289e-03 4.107e-03 4.158e-03 2.919e-02
summary(stats::cooks.distance(GAD_n_gender))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 4.060e-06 4.094e-04 1.039e-03 3.223e-03 2.729e-03 6.962e-02
summary(stats::cooks.distance(GAD_n_year))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000032 0.0003737 0.0016544 0.0035522 0.0044143 0.0387202
#NO OUTLIER

summary(stats::cooks.distance(PHQ_UGPG))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.999e-05 5.677e-04 2.497e-03 4.187e-03 5.343e-03 5.263e-02
summary(stats::cooks.distance(PHQ_n_disability))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 2.417e-05 4.093e-04 1.432e-03 4.662e-03 3.713e-03 7.270e-02
summary(stats::cooks.distance(PHQ_n_ethnicity))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 2.000e-08 8.339e-04 2.173e-03 4.375e-03 4.878e-03 4.251e-02
summary(stats::cooks.distance(PHQ_n_orientation))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 3.587e-06 4.073e-04 1.786e-03 4.084e-03 4.879e-03 2.890e-02
summary(stats::cooks.distance(PHQ_n_gender))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 3.300e-07 3.787e-04 1.260e-03 3.170e-03 2.904e-03 7.683e-02
summary(stats::cooks.distance(PHQ_n_year))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.190e-06 5.345e-04 1.438e-03 3.883e-03 3.715e-03 5.741e-02
#NO OUTLIER

plot(PHQ_UGPG)

plot(PHQ_n_disability)

plot(PHQ_n_ethnicity)

plot(PHQ_n_orientation)

plot(PHQ_n_gender)

plot(PHQ_n_year)

### PHQ table demo ####
PHQ_UGPGt <- export_summs(PHQ_UGPG, scale = TRUE)
PHQ_n_ethnicityt <- export_summs(PHQ_n_ethnicity,scale = TRUE)
PHQ_n_gendert <-export_summs(PHQ_n_gender,scale = TRUE)
PHQ_n_disabilityt <- export_summs(PHQ_n_disability,scale = TRUE)
PHQ_n_orientationt <- export_summs(PHQ_n_orientation,scale = TRUE)
PHQ_n_yeart <- export_summs(PHQ_n_year,scale = TRUE)
PHQ_student.statust <- export_summs(PHQ_student.status,scale = TRUE)
PHQ_Multiplet <- export_summs(PHQ_Multiple,scale = TRUE)

PHQ_UGPGr <- filter(PHQ_UGPGt, names == "R2")
PHQ_n_ethnicityr <- filter(PHQ_n_ethnicityt, names == "R2")
PHQ_n_genderr <- filter(PHQ_n_gendert, names == "R2")
PHQ_n_disabilityr <- filter(PHQ_n_disabilityt, names == "R2")
PHQ_n_orientationr <- filter(PHQ_n_orientationt, names == "R2")
PHQ_n_yearr <- filter(PHQ_n_yeart, names == "R2")
PHQ_student.statusr <- filter(PHQ_student.statust, names == "R2")
PHQ_Multipler <- filter(PHQ_Multiplet, names == "R2")

PHQ_UGPGr <- PHQ_UGPGr[,2]
PHQ_n_ethnicityr <- PHQ_n_ethnicityr[,2]
PHQ_n_genderr <-PHQ_n_genderr[,2]
PHQ_n_disabilityr <- PHQ_n_disabilityr[,2]
PHQ_n_orientationr <- PHQ_n_orientationr[,2]
PHQ_n_yearr <- PHQ_n_yearr[,2]
PHQ_student.statusr <- PHQ_student.statusr[,2]
PHQ_Multipler <- PHQ_Multipler[,2]

PHQ_UGPGr$model <- "UGPG"
PHQ_n_ethnicityr$model <- "ethnicity"
PHQ_n_genderr$model <- "gender"
PHQ_n_disabilityr$model  <- "disability"
PHQ_n_orientationr$model <- "orientation"
PHQ_n_yearr$model <- "year"
PHQ_student.statusr$model  <- "student.status"
PHQ_Multipler$model <- "Multiple"

rsquared_demo <- rbind(PHQ_UGPGr, PHQ_student.statusr,
                       PHQ_n_ethnicityr, PHQ_n_disabilityr, PHQ_n_orientationr,
                       PHQ_n_genderr,PHQ_n_yearr)
colnames(rsquared_demo)<- c("rsquared", "model")

PHQ_UGPGn <- filter(PHQ_UGPGt, names == "N")
PHQ_n_ethnicityn <- filter(PHQ_n_ethnicityt, names == "N")
PHQ_n_gendern <- filter(PHQ_n_gendert, names == "N")
PHQ_n_disabilityn <- filter(PHQ_n_disabilityt, names == "N")
PHQ_n_orientationn <- filter(PHQ_n_orientationt, names == "N")
PHQ_n_yearn <- filter(PHQ_n_yeart, names == "N")
PHQ_student.statusn <- filter(PHQ_student.statust, names == "N")
PHQ_Multiplen <- filter(PHQ_Multiplet, names == "N")

PHQ_UGPGn <- PHQ_UGPGn[,2]
PHQ_n_ethnicityn <- PHQ_n_ethnicityn[,2]
PHQ_n_gendern <-PHQ_n_gendern[,2]
PHQ_n_disabilityn <- PHQ_n_disabilityn[,2]
PHQ_n_orientationn <- PHQ_n_orientationn[,2]
PHQ_n_yearn <- PHQ_n_yearn[,2]
PHQ_student.statusn <- PHQ_student.statusn[,2]
PHQ_Multiplen <- PHQ_Multiplen[,2]

PHQ_UGPGn$model <- "UGPG"
PHQ_n_ethnicityn$model <- "ethnicity"
PHQ_n_gendern$model <- "gender"
PHQ_n_disabilityn$model  <- "disability"
PHQ_n_orientationn$model <- "orientation"
PHQ_n_yearn$model <- "year"
PHQ_student.statusn$model  <- "student.status"
PHQ_Multiplen$model <- "Multiple"



n_demo <- rbind(PHQ_UGPGn, PHQ_student.statusn, 
                PHQ_n_ethnicityn, PHQ_n_disabilityn, PHQ_n_orientationn,
                PHQ_n_gendern,PHQ_n_yearn)

colnames(n_demo)<- c("n", "model")

PHQ_UGPGcol <-tidy(PHQ_UGPG) %>%mutate(model = "UGPG")
PHQ_n_ethnicitycol <-tidy(PHQ_n_ethnicity) %>% mutate(model = "ethnicity")
PHQ_n_gendercol <-tidy(PHQ_n_gender) %>% mutate(model = "gender")
PHQ_n_disabilitycol <-tidy(PHQ_n_disability) %>% mutate(model = "disability")
PHQ_n_orientationcol <-tidy(PHQ_n_orientation) %>% mutate(model = "orientation")
PHQ_n_yearcol <-tidy(PHQ_n_year) %>% mutate(model = "year")
PHQ_student.statuscol <-tidy(PHQ_student.status) %>% 
  mutate(model = "student.status")

PHQ_UGPGcol[PHQ_UGPGcol == "(Intercept)"] <- "UGPG"
PHQ_n_ethnicitycol[PHQ_n_ethnicitycol == "(Intercept)"] <- "n_ethnicity"
PHQ_n_gendercol[PHQ_n_gendercol == "(Intercept)"] <- "n_gender"
PHQ_n_disabilitycol[PHQ_n_disabilitycol== "(Intercept)"] <- "n_disability"
PHQ_n_orientationcol[PHQ_n_orientationcol == "(Intercept)"] <- "n_orientation"
PHQ_n_yearcol[PHQ_n_yearcol == "(Intercept)"] <- "n_year"
PHQ_student.statuscol[PHQ_student.statuscol== "(Intercept)"] <- "student.status"



all_models_demo<- rbind(PHQ_UGPGcol,
                        PHQ_n_ethnicitycol,
                        PHQ_n_gendercol,
                        PHQ_n_disabilitycol,
                        PHQ_n_orientationcol,
                        PHQ_n_yearcol,
                        PHQ_student.statuscol)


PHQ_Multiple_col <-  tidy(PHQ_Multiple) %>% mutate(model = "Multiple")

finaltable1_PHQ_demo <- right_join( all_models_demo, rsquared_demo)
## Joining with `by = join_by(model)`
finaltable1_PHQ_demo <- right_join( n_demo, finaltable1_PHQ_demo )
## Joining with `by = join_by(model)`
## Warning in right_join(n_demo, finaltable1_PHQ_demo): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable1_PHQ_demo <- as.data.frame(finaltable1_PHQ_demo)
colnames(finaltable1_PHQ_demo)<- c("N","Model", "term", "beta", "SE", "t", "p", 
                                   "rsquared" )
finaltable1_PHQ_demo$beta <- round(finaltable1_PHQ_demo$beta, digits = 2)
finaltable1_PHQ_demo$rsquared <- as.numeric(finaltable1_PHQ_demo$rsquared)
finaltable1_PHQ_demo$rsquared <- round(finaltable1_PHQ_demo$rsquared, digits = 2)
finaltable1_PHQ_demo$p <- round(finaltable1_PHQ_demo$p, digits = 3)
finaltable1_PHQ_demo <- finaltable1_PHQ_demo[,c(3,1,4,8,7)]
finaltable1_PHQ_demo$criterion <- "PHQ"


#### GAD tables demo####
GAD_UGPGt <- export_summs(GAD_UGPG, scale = TRUE)
GAD_n_ethnicityt <- export_summs(GAD_n_ethnicity,scale = TRUE)
GAD_n_gendert <-export_summs(GAD_n_gender,scale = TRUE)
GAD_n_disabilityt <- export_summs(GAD_n_disability,scale = TRUE)
GAD_n_orientationt <- export_summs(GAD_n_orientation,scale = TRUE)
GAD_n_yeart <- export_summs(GAD_n_year,scale = TRUE)
GAD_student.statust <- export_summs(GAD_student.status,scale = TRUE)
GAD_Multiplet <- export_summs(GAD_Multiple,scale = TRUE)


GAD_UGPGr <- filter(GAD_UGPGt, names == "R2")
GAD_n_ethnicityr <- filter(GAD_n_ethnicityt, names == "R2")
GAD_n_genderr <- filter(GAD_n_gendert, names == "R2")
GAD_n_disabilityr <- filter(GAD_n_disabilityt, names == "R2")
GAD_n_orientationr <- filter(GAD_n_orientationt, names == "R2")
GAD_n_yearr <- filter(GAD_n_yeart, names == "R2")
GAD_student.statusr <- filter(GAD_student.statust, names == "R2")
GAD_Multipler <- filter(GAD_Multiplet, names == "R2")

GAD_UGPGr <- GAD_UGPGr[,2]
GAD_n_ethnicityr <- GAD_n_ethnicityr[,2]
GAD_n_genderr <-GAD_n_genderr[,2]
GAD_n_disabilityr <- GAD_n_disabilityr[,2]
GAD_n_orientationr <- GAD_n_orientationr[,2]
GAD_n_yearr <- GAD_n_yearr[,2]
GAD_student.statusr <- GAD_student.statusr[,2]
GAD_Multipler <- GAD_Multipler[,2]

GAD_UGPGr$model <- "UGPG"
GAD_n_ethnicityr$model <- "ethnicity"
GAD_n_genderr$model <- "gender"
GAD_n_disabilityr$model  <- "disability"
GAD_n_orientationr$model <- "orientation"
GAD_n_yearr$model <- "year"
GAD_student.statusr$model  <- "student.status"
GAD_Multipler$model <- "Multiple"


rsquared_demo_GAD <- rbind(GAD_UGPGr, GAD_student.statusr, 
                           GAD_n_ethnicityr, GAD_n_disabilityr, GAD_n_orientationr,
                           GAD_n_genderr,GAD_n_yearr)

colnames(rsquared_demo_GAD)<- c("rsquared", "model")

GAD_UGPGn <- filter(GAD_UGPGt, names == "N")
GAD_n_ethnicityn <- filter(GAD_n_ethnicityt, names == "N")
GAD_n_gendern <- filter(GAD_n_gendert, names == "N")
GAD_n_disabilityn <- filter(GAD_n_disabilityt, names == "N")
GAD_n_orientationn <- filter(GAD_n_orientationt, names == "N")
GAD_n_yearn <- filter(GAD_n_yeart, names == "N")
GAD_student.statusn <- filter(GAD_student.statust, names == "N")
GAD_Multiplen <- filter(GAD_Multiplet, names == "N")

GAD_UGPGn <- GAD_UGPGn[,2]
GAD_n_ethnicityn <- GAD_n_ethnicityn[,2]
GAD_n_gendern <-GAD_n_gendern[,2]
GAD_n_disabilityn <- GAD_n_disabilityn[,2]
GAD_n_orientationn <- GAD_n_orientationn[,2]
GAD_n_yearn <- GAD_n_yearn[,2]
GAD_student.statusn <- GAD_student.statusn[,2]
GAD_Multiplen <- GAD_Multiplen[,2]

GAD_UGPGn$model <- "UGPG"
GAD_n_ethnicityn$model <- "ethnicity"
GAD_n_gendern$model <- "gender"
GAD_n_disabilityn$model  <- "disability"
GAD_n_orientationn$model <- "orientation"
GAD_n_yearn$model <- "year"
GAD_student.statusn$model  <- "student.status"
GAD_Multiplen$model <- "Multiple"


n_demo_GAD <- rbind(GAD_UGPGn, GAD_student.statusn, GAD_n_disabilityn, 
                    GAD_n_ethnicityn, GAD_n_orientationn,
                    GAD_n_gendern,GAD_n_yearn)

colnames(n_demo_GAD)<- c("n", "model")


GAD_UGPGcol <-tidy(GAD_UGPG) %>% mutate(model = "UGPG")
GAD_n_ethnicitycol <-tidy(GAD_n_ethnicity) %>% mutate(model = "ethnicity")
GAD_n_gendercol <-tidy(GAD_n_gender) %>% mutate(model = "gender")
GAD_n_disabilitycol <-tidy(GAD_n_disability) %>% mutate(model = "disability")
GAD_n_orientationcol <-tidy(GAD_n_orientation) %>% mutate(model = "orientation")
GAD_n_yearcol <-tidy(GAD_n_year) %>% mutate(model = "year")
GAD_student.statuscol <-tidy(GAD_student.status) %>% mutate(model = "student.status")

GAD_UGPGcol[GAD_UGPGcol == "(Intercept)"] <- "UGPG"
GAD_n_ethnicitycol[GAD_n_ethnicitycol == "(Intercept)"] <- "n_ethnicity"
GAD_n_gendercol[GAD_n_gendercol == "(Intercept)"] <- "n_gender"
GAD_n_disabilitycol[GAD_n_disabilitycol== "(Intercept)"] <- "n_disability"
GAD_n_orientationcol[GAD_n_orientationcol == "(Intercept)"] <- "n_orientation"
GAD_n_yearcol[GAD_n_yearcol == "(Intercept)"] <- "n_year"
GAD_student.statuscol[GAD_student.statuscol== "(Intercept)"] <- "student.status"



all_models_demo_GAD <- rbind(GAD_UGPGcol,
                             GAD_n_ethnicitycol,
                             GAD_n_gendercol,
                             GAD_n_disabilitycol,
                             GAD_n_orientationcol,
                             GAD_n_yearcol,
                             GAD_student.statuscol)

GAD_Multiple_col <-  tidy(GAD_Multiple) %>% mutate(model = "Multiple")


finaltable1_GAD_demo <- right_join(all_models_demo_GAD, rsquared_demo_GAD)
## Joining with `by = join_by(model)`
finaltable1_GAD_demo <- right_join(n_demo_GAD, finaltable1_GAD_demo)
## Joining with `by = join_by(model)`
## Warning in right_join(n_demo_GAD, finaltable1_GAD_demo): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable1_GAD_demo <- as.data.frame(finaltable1_GAD_demo)
colnames(finaltable1_GAD_demo)<- c("N1","Model", "term", "beta1", "SE1", "t1", "p1", 
                                   "rsquared1")
finaltable1_GAD_demo$beta1 <- round(finaltable1_GAD_demo$beta1, digits = 2)
finaltable1_GAD_demo$rsquared1 <- as.numeric(finaltable1_GAD_demo$rsquared1)
finaltable1_GAD_demo$rsquared1 <- round(finaltable1_GAD_demo$rsquared1, 
                                        digits = 2)
finaltable1_GAD_demo$p1 <- as.numeric(finaltable1_GAD_demo$p1)
finaltable1_GAD_demo$p1 <- round(finaltable1_GAD_demo$p1, digits = 3)
finaltable1_GAD_demo <- finaltable1_GAD_demo[,c(3,1,4,8,7)]
finaltable1_GAD_demo$criterion1 <- "GAD"

final_table1_demo <- right_join(finaltable1_GAD_demo,finaltable1_PHQ_demo)
## Joining with `by = join_by(term)`
#when there is a variable without a number it is the intercept

colnames(GAD_Multipler)<- c("rsquared", "model")
colnames(GAD_Multiplen)<- c("n", "model")

finaltable1_GAD_Multiple <- right_join(GAD_Multiple_col, GAD_Multipler)
## Joining with `by = join_by(model)`
finaltable1_GAD_Multiple <- right_join(GAD_Multiplen, finaltable1_GAD_Multiple)
## Joining with `by = join_by(model)`
## Warning in right_join(GAD_Multiplen, finaltable1_GAD_Multiple): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable1_GAD_Multiple <- as.data.frame(finaltable1_GAD_Multiple)
colnames(finaltable1_GAD_Multiple)<- c("N1","Model", "term", "beta1", "SE1", "t1", "p1", 
                                       "rsquared1")
finaltable1_GAD_Multiple$beta1 <- round(finaltable1_GAD_Multiple$beta1, digits = 2)
finaltable1_GAD_Multiple$rsquared1 <- as.numeric(finaltable1_GAD_Multiple$rsquared1)
finaltable1_GAD_Multiple$rsquared1 <- round(finaltable1_GAD_Multiple$rsquared1, digits = 2)
finaltable1_GAD_Multiple$p1 <- round(finaltable1_GAD_Multiple$p1, digits = 3)
finaltable1_GAD_Multiple <- finaltable1_GAD_Multiple[,c(3,1,4,7,8)]
finaltable1_GAD_Multiple$criterion1 <- "GAD"

colnames(PHQ_Multipler)<- c("rsquared", "model")
colnames(PHQ_Multiplen)<- c("n", "model")

finaltable1_PHQ_Multiple <- right_join(PHQ_Multiple_col, PHQ_Multipler)
## Joining with `by = join_by(model)`
finaltable1_PHQ_Multiple <- right_join(PHQ_Multiplen, finaltable1_PHQ_Multiple)
## Joining with `by = join_by(model)`
## Warning in right_join(PHQ_Multiplen, finaltable1_PHQ_Multiple): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable1_PHQ_Multiple <- as.data.frame(finaltable1_PHQ_Multiple)
colnames(finaltable1_PHQ_Multiple)<- c("N","Model", "term", "beta", "SE", "t", "p", 
                                       "rsquared")
finaltable1_PHQ_Multiple$beta <- round(finaltable1_PHQ_Multiple$beta, digits = 2)
finaltable1_PHQ_Multiple$rsquared <- as.numeric(finaltable1_PHQ_Multiple$rsquared)
finaltable1_PHQ_Multiple$rsquared <- round(finaltable1_PHQ_Multiple$rsquared, digits = 2)
finaltable1_PHQ_Multiple$p <- round(finaltable1_PHQ_Multiple$p, digits = 3)
finaltable1_PHQ_Multiple <- finaltable1_PHQ_Multiple[,c(3,1,4,7,8)]
finaltable1_PHQ_Multiple$criterion <- "PHQ"

final_table1_demo_multiple <- right_join(finaltable1_GAD_Multiple,
                                         finaltable1_PHQ_Multiple)
## Joining with `by = join_by(term)`
knitr::kable(final_table1_demo, "pipe", caption = "Simple Linear Regressions with Demographics")
Simple Linear Regressions with Demographics
term N1 beta1 rsquared1 p1 criterion1 N beta rsquared p criterion
UGPG 242 0.02 0.00 0.768 GAD 242 0.08 0.01 0.316 PHQ
UGPG1 242 -0.08 0.00 0.577 GAD 242 -0.24 0.01 0.079 PHQ
student.status 242 0.08 0.01 0.404 GAD 242 0.12 0.02 0.185 PHQ
student.status1 242 -0.26 0.01 0.112 GAD 242 -0.34 0.02 0.040 PHQ
student.status2 242 -0.08 0.01 0.609 GAD 242 -0.18 0.02 0.235 PHQ
n_disability 230 -0.18 0.10 0.009 GAD 230 -0.21 0.13 0.002 PHQ
n_disability1 230 0.86 0.10 0.000 GAD 230 0.95 0.13 0.000 PHQ
n_ethnicity 240 0.01 0.00 0.948 GAD 240 0.00 0.00 0.974 PHQ
n_ethnicity1 240 -0.03 0.00 0.840 GAD 240 -0.02 0.00 0.907 PHQ
n_orientation 231 -0.14 0.05 0.072 GAD 231 -0.13 0.05 0.080 PHQ
n_orientationSexual Minority 231 0.49 0.05 0.001 GAD 231 0.49 0.05 0.001 PHQ
n_gender 242 -0.12 0.00 0.535 GAD 242 -0.07 0.02 0.706 PHQ
n_gender1 242 0.40 0.00 0.415 GAD 242 0.91 0.02 0.062 PHQ
n_gender2 242 0.12 0.00 0.541 GAD 242 0.06 0.02 0.777 PHQ
n_year 242 0.15 0.05 0.070 GAD 242 0.05 0.03 0.548 PHQ
n_yearYear 2 242 -0.23 0.05 0.204 GAD 242 -0.12 0.03 0.507 PHQ
n_yearYear 3 242 -0.29 0.05 0.097 GAD 242 0.08 0.03 0.665 PHQ
n_yearYear 4+ 242 -0.79 0.05 0.001 GAD 242 -0.54 0.03 0.021 PHQ
knitr::kable(final_table1_demo_multiple, "pipe", caption = "Multiple Linear Regression with Demographics")
Multiple Linear Regression with Demographics
term N1 beta1 p1 rsquared1 criterion1 N beta p rsquared criterion
(Intercept) 217 0.42 0.308 0.2 GAD 217 0.65 0.113 0.2 PHQ
n_disability1 217 0.77 0.000 0.2 GAD 217 0.85 0.000 0.2 PHQ
student.status1 217 -0.31 0.076 0.2 GAD 217 -0.33 0.055 0.2 PHQ
student.status2 217 -0.06 0.725 0.2 GAD 217 -0.15 0.394 0.2 PHQ
n_orientationSexual Minority 217 0.31 0.047 0.2 GAD 217 0.22 0.158 0.2 PHQ
n_ethnicity1 217 -0.03 0.861 0.2 GAD 217 0.08 0.579 0.2 PHQ
n_gender1 217 -0.38 0.477 0.2 GAD 217 0.22 0.681 0.2 PHQ
n_gender2 217 0.11 0.585 0.2 GAD 217 0.04 0.849 0.2 PHQ
UGPG1 217 0.04 0.835 0.2 GAD 217 -0.03 0.861 0.2 PHQ
n_yearYear 2 217 -0.35 0.077 0.2 GAD 217 -0.21 0.276 0.2 PHQ
n_yearYear 3 217 -0.39 0.040 0.2 GAD 217 -0.01 0.970 0.2 PHQ
n_yearYear 4+ 217 -0.71 0.004 0.2 GAD 217 -0.35 0.161 0.2 PHQ
age 217 -0.02 0.199 0.2 GAD 217 -0.04 0.042 0.2 PHQ

Linear Regression with Scales

###GAD lm####

GAD_PHQ <- lm(GADz~ PHQz, pass)
GAD_CUDIT <- lm(GADz~ CUDITz, pass)
GAD_AUDIT <- lm(GADz~ AUDITz, pass)
GAD_unil <- lm(GADz~ unil_z, pass)
GAD_prel <- lm(GADz~ prel_z, pass)
GAD_SA <- lm(GADz~ SAz, pass)
GAD_SCI <- lm(GADz~ SCInz, pass)
GAD_PS <- lm(GADz~ PSz, pass)
GAD_wellbeing <- lm(GADz~ wellbeingz, pass)
GAD_SC <- lm(GADz~ SCz, pass)
GAD_perfectionism <- lm(GADz~ perfectionismz, pass)
GAD_aca<- lm(GADz~SE_aca_z, pass)
GAD_fi<- lm(GADz~SE_fi_z, pass)
GAD_acc<- lm(GADz~SE_acc_z, pass)
GAD_fr<- lm(GADz~SE_fr_z, pass)
GAD_co<- lm(GADz~SE_co_z, pass)

GAD_PHQ1<- lm(GADz~ PHQz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
GAD_CUDIT1 <- lm(GADz~ CUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age, pass)
GAD_AUDIT1 <- lm(GADz~ AUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age, pass)
GAD_unil1 <- lm(GADz~ unil_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age, pass)
GAD_prel1 <- lm(GADz~ prel_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age, pass)
GAD_SA1 <- lm(GADz~ SAz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
GAD_SCI1 <- lm(GADz~ SCInz+n_disability+student.status+
                 n_orientation+n_ethnicity+n_gender+
                 UGPG+n_year+age, pass)
GAD_PS1 <- lm(GADz~ PSz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
GAD_wellbeing1 <- lm(GADz~ wellbeingz+n_disability+student.status+
                       n_orientation+n_ethnicity+n_gender+
                       UGPG+n_year+age, pass)
GAD_SC1 <- lm(GADz~ SCz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
GAD_perfectionism1 <- lm(GADz~ perfectionismz+n_disability+student.status+
                           n_orientation+n_ethnicity+n_gender+
                           UGPG+n_year+age, pass)
GAD_aca1<- lm(GADz~SE_aca_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
GAD_fi1<- lm(GADz~SE_fi_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age, pass)
GAD_acc1<- lm(GADz~SE_acc_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
GAD_fr1<- lm(GADz~SE_fr_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age, pass)
GAD_co1<- lm(GADz~SE_co_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age, pass)


GAD_PHQ2<- lm(GADz~ PHQz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
GAD_CUDIT2 <- lm(GADz~ CUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age+SE_fi_z, pass)
GAD_AUDIT2 <- lm(GADz~ AUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age+SE_fi_z, pass)
GAD_unil2 <- lm(GADz~ unil_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age+SE_fi_z, pass)
GAD_prel2 <- lm(GADz~ prel_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age+SE_fi_z, pass)
GAD_SA2 <- lm(GADz~ SAz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
GAD_SCI2 <- lm(GADz~ SCInz+n_disability+student.status+
                 n_orientation+n_ethnicity+n_gender+
                 UGPG+n_year+age+SE_fi_z, pass)
GAD_PS2 <- lm(GADz~ PSz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
GAD_wellbeing2 <- lm(GADz~ wellbeingz+n_disability+student.status+
                       n_orientation+n_ethnicity+n_gender+
                       UGPG+n_year+age+SE_fi_z, pass)
GAD_SC2 <- lm(GADz~ SCz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
GAD_perfectionism2 <- lm(GADz~ perfectionismz+n_disability+student.status+
                           n_orientation+n_ethnicity+n_gender+
                           UGPG+n_year+age+SE_fi_z, pass)
GAD_aca2<- lm(GADz~SE_aca_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
GAD_acc2<- lm(GADz~SE_acc_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
GAD_fr2<- lm(GADz~SE_fr_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age+SE_fi_z, pass)
GAD_co2<- lm(GADz~SE_co_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age+SE_fi_z, pass)

####PHQ lm####
PHQ_GAD <- lm(PHQz~ GADz, pass)
PHQ_CUDIT <- lm(PHQz~ CUDITz, pass)
PHQ_AUDIT <- lm(PHQz~ AUDITz, pass)
PHQ_unil <- lm(PHQz~ unil_z, pass)
PHQ_prel <- lm(PHQz~ prel_z, pass)
PHQ_SA <- lm(PHQz~ SAz, pass)
PHQ_SCI <- lm(PHQz~ SCInz, pass)
PHQ_PS <- lm(PHQz~ PSz, pass)
PHQ_wellbeing <- lm(PHQz~ wellbeingz, pass)
PHQ_SC <- lm(PHQz~ SCz, pass)
PHQ_perfectionism <- lm(PHQz~ perfectionismz, pass)
PHQ_aca<- lm(PHQz~SE_aca_z, pass)
PHQ_fi<- lm(PHQz~SE_fi_z, pass)
PHQ_acc<- lm(PHQz~SE_acc_z, pass)
PHQ_fr<- lm(PHQz~SE_fr_z, pass)
PHQ_co<- lm(PHQz~SE_co_z, pass)

PHQ_GAD1 <- lm(PHQz~ GADz+n_disability+student.status+
                 n_orientation+n_ethnicity+n_gender+
                 UGPG+n_year+age, pass)
PHQ_CUDIT1 <- lm(PHQz~ CUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age, pass)
PHQ_AUDIT1 <- lm(PHQz~ AUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age, pass)
PHQ_unil1 <- lm(PHQz~ unil_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age, pass)
PHQ_prel1 <- lm(PHQz~ prel_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age, pass)
PHQ_SA1 <- lm(PHQz~ SAz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
PHQ_SCI1 <- lm(PHQz~ SCInz+n_disability+student.status+
                 n_orientation+n_ethnicity+n_gender+
                 UGPG+n_year+age, pass)
PHQ_PS1 <- lm(PHQz~ PSz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
PHQ_wellbeing1 <- lm(PHQz~ wellbeingz+n_disability+student.status+
                       n_orientation+n_ethnicity+n_gender+
                       UGPG+n_year+age, pass)
PHQ_SC1 <- lm(PHQz~ SCz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
PHQ_perfectionism1 <- lm(PHQz~ perfectionismz+n_disability+student.status+
                           n_orientation+n_ethnicity+n_gender+
                           UGPG+n_year+age, pass)
PHQ_aca1<- lm(PHQz~SE_aca_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
PHQ_fi1<- lm(PHQz~SE_fi_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age, pass)
PHQ_acc1<- lm(PHQz~SE_acc_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age, pass)
PHQ_fr1<- lm(PHQz~SE_fr_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age, pass)
PHQ_co1<- lm(PHQz~SE_co_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age, pass)


PHQ_GAD2 <- lm(PHQz~ GADz+n_disability+student.status+
                 n_orientation+n_ethnicity+n_gender+
                 UGPG+n_year+age+SE_fi_z, pass)
PHQ_CUDIT2 <- lm(PHQz~ CUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age+SE_fi_z, pass)
PHQ_AUDIT2 <- lm(PHQz~ AUDITz+n_disability+student.status+
                   n_orientation+n_ethnicity+n_gender+
                   UGPG+n_year+age+SE_fi_z, pass)
PHQ_unil2 <- lm(PHQz~ unil_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age+SE_fi_z, pass)
PHQ_prel2 <- lm(PHQz~ prel_z+n_disability+student.status+
                  n_orientation+n_ethnicity+n_gender+
                  UGPG+n_year+age+SE_fi_z, pass)
PHQ_SA2 <- lm(PHQz~ SAz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
PHQ_SCI2 <- lm(PHQz~ SCInz+n_disability+student.status+
                 n_orientation+n_ethnicity+n_gender+
                 UGPG+n_year+age+SE_fi_z, pass)
PHQ_PS2 <- lm(PHQz~ PSz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
PHQ_wellbeing2 <- lm(PHQz~ wellbeingz+n_disability+student.status+
                       n_orientation+n_ethnicity+n_gender+
                       UGPG+n_year+age+SE_fi_z, pass)
PHQ_SC2 <- lm(PHQz~ SCz+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
PHQ_perfectionism2 <- lm(PHQz~ perfectionismz+n_disability+student.status+
                           n_orientation+n_ethnicity+n_gender+
                           UGPG+n_year+age+SE_fi_z, pass)
PHQ_aca2<- lm(PHQz~SE_aca_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
PHQ_fi2<- lm(PHQz~SE_fi_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age+SE_fi_z, pass)
PHQ_acc2<- lm(PHQz~SE_acc_z+n_disability+student.status+
                n_orientation+n_ethnicity+n_gender+
                UGPG+n_year+age+SE_fi_z, pass)
PHQ_fr2<- lm(PHQz~SE_fr_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age+SE_fi_z, pass)
PHQ_co2<- lm(PHQz~SE_co_z+n_disability+student.status+
               n_orientation+n_ethnicity+n_gender+
               UGPG+n_year+age+SE_fi_z, pass)

#### summaries GAD####
summary(GAD_PHQ)
## 
## Call:
## lm(formula = GADz ~ PHQz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.88121 -0.38163 -0.09044  0.37534  1.88189 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0003387  0.0406555  -0.008    0.993    
## PHQz         0.7725357  0.0405401  19.056   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6324 on 240 degrees of freedom
## Multiple R-squared:  0.6021, Adjusted R-squared:  0.6004 
## F-statistic: 363.1 on 1 and 240 DF,  p-value: < 2.2e-16
summary(GAD_CUDIT)
## 
## Call:
## lm(formula = GADz ~ CUDITz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5099 -0.7852 -0.2571  0.6850  2.4850 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01329    0.09204   0.144    0.885
## CUDITz       0.14045    0.09334   1.505    0.135
## 
## Residual standard error: 0.9784 on 111 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.01999,    Adjusted R-squared:  0.01116 
## F-statistic: 2.264 on 1 and 111 DF,  p-value: 0.1353
summary(GAD_AUDIT)
## 
## Call:
## lm(formula = GADz ~ AUDITz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3787 -0.7660 -0.2902  0.6572  2.4903 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001762   0.064245  -0.027    0.978
## AUDITz       0.080502   0.064126   1.255    0.211
## 
## Residual standard error: 0.9993 on 240 degrees of freedom
## Multiple R-squared:  0.006524,   Adjusted R-squared:  0.002384 
## F-statistic: 1.576 on 1 and 240 DF,  p-value: 0.2106
summary(GAD_unil)
## 
## Call:
## lm(formula = GADz ~ unil_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7564 -0.6638 -0.1739  0.6029  2.6261 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.005845   0.058680  -0.100    0.921    
## unil_z       0.420251   0.059717   7.037 2.04e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9128 on 240 degrees of freedom
## Multiple R-squared:  0.1711, Adjusted R-squared:  0.1676 
## F-statistic: 49.52 on 1 and 240 DF,  p-value: 2.04e-11
summary(GAD_prel)
## 
## Call:
## lm(formula = GADz ~ prel_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4997 -0.6830 -0.2830  0.5839  2.9126 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.004185   0.059436  -0.070    0.944    
## prel_z       0.391060   0.060202   6.496 4.72e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9246 on 240 degrees of freedom
## Multiple R-squared:  0.1495, Adjusted R-squared:  0.146 
## F-statistic: 42.19 on 1 and 240 DF,  p-value: 4.719e-10
summary(GAD_SA)
## 
## Call:
## lm(formula = GADz ~ SAz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8988 -0.5590 -0.1590  0.5292  2.3060 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.005643   0.053012   0.106    0.915    
## SAz         0.558255   0.052099  10.715   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8246 on 240 degrees of freedom
## Multiple R-squared:  0.3236, Adjusted R-squared:  0.3208 
## F-statistic: 114.8 on 1 and 240 DF,  p-value: < 2.2e-16
summary(GAD_SCI)
## 
## Call:
## lm(formula = GADz ~ SCInz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9242 -0.5794 -0.1526  0.5301  2.4207 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.02065    0.05231  -0.395    0.693    
## SCInz        0.59055    0.05215  11.324   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8035 on 234 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.354,  Adjusted R-squared:  0.3512 
## F-statistic: 128.2 on 1 and 234 DF,  p-value: < 2.2e-16
summary(GAD_PS)
## 
## Call:
## lm(formula = GADz ~ PSz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.83765 -0.45118 -0.05364  0.44882  2.07149 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.007869   0.047893  -0.164     0.87    
## PSz          0.681897   0.047970  14.215   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7389 on 236 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.4613, Adjusted R-squared:  0.459 
## F-statistic: 202.1 on 1 and 236 DF,  p-value: < 2.2e-16
summary(GAD_wellbeing)
## 
## Call:
## lm(formula = GADz ~ wellbeingz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.47449 -0.51610 -0.07214  0.41494  2.40471 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.004709   0.047777  -0.099    0.922    
## wellbeingz  -0.684563   0.047905 -14.290   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7371 on 236 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.4639, Adjusted R-squared:  0.4616 
## F-statistic: 204.2 on 1 and 236 DF,  p-value: < 2.2e-16
summary(GAD_SC)
## 
## Call:
## lm(formula = GADz ~ SCz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4399 -0.7899 -0.2735  0.6785  2.3928 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003019   0.064111  -0.047    0.962
## SCz         -0.102078   0.064049  -1.594    0.112
## 
## Residual standard error: 0.9973 on 240 degrees of freedom
## Multiple R-squared:  0.01047,    Adjusted R-squared:  0.00635 
## F-statistic:  2.54 on 1 and 240 DF,  p-value: 0.1123
summary(GAD_perfectionism)
## 
## Call:
## lm(formula = GADz ~ perfectionismz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7272 -0.7078 -0.1744  0.6280  2.3789 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.01950    0.05941  -0.328    0.743    
## perfectionismz  0.40607    0.05939   6.838 6.95e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9127 on 234 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.1665, Adjusted R-squared:  0.163 
## F-statistic: 46.75 on 1 and 234 DF,  p-value: 6.949e-11
summary(GAD_aca)
## 
## Call:
## lm(formula = GADz ~ SE_aca_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6568 -0.7197 -0.1353  0.5864  2.4701 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.00258    0.05970  -0.043    0.966    
## SE_aca_z    -0.37835    0.06007  -6.299 1.42e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9288 on 240 degrees of freedom
## Multiple R-squared:  0.1419, Adjusted R-squared:  0.1383 
## F-statistic: 39.67 on 1 and 240 DF,  p-value: 1.421e-09
summary(GAD_fi)
## 
## Call:
## lm(formula = GADz ~ SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8313 -0.6978 -0.2498  0.6617  2.4699 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.001571   0.060218  -0.026    0.979    
## SE_fi_z      0.355239   0.060120   5.909 1.17e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9368 on 240 degrees of freedom
## Multiple R-squared:  0.127,  Adjusted R-squared:  0.1234 
## F-statistic: 34.91 on 1 and 240 DF,  p-value: 1.174e-08
summary(GAD_acc)
## 
## Call:
## lm(formula = GADz ~ SE_acc_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6568 -0.7197 -0.1353  0.5864  2.4701 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.00258    0.05970  -0.043    0.966    
## SE_acc_z    -0.37835    0.06007  -6.299 1.42e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9288 on 240 degrees of freedom
## Multiple R-squared:  0.1419, Adjusted R-squared:  0.1383 
## F-statistic: 39.67 on 1 and 240 DF,  p-value: 1.421e-09
summary(GAD_fr)
## 
## Call:
## lm(formula = GADz ~ SE_fr_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6588 -0.8128 -0.2560  0.7411  2.5673 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.004216   0.062961  -0.067 0.946663    
## SE_fr_z     -0.213570   0.062996  -3.390 0.000816 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9794 on 240 degrees of freedom
## Multiple R-squared:  0.0457, Adjusted R-squared:  0.04173 
## F-statistic: 11.49 on 1 and 240 DF,  p-value: 0.0008164
summary(GAD_co)
## 
## Call:
## lm(formula = GADz ~ SE_co_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4541 -0.7378 -0.2368  0.7252  2.5683 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.004029   0.063533  -0.063 0.949485    
## SE_co_z     -0.234682   0.063829  -3.677 0.000294 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9718 on 232 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.05506,    Adjusted R-squared:  0.05099 
## F-statistic: 13.52 on 1 and 232 DF,  p-value: 0.0002935
summary(GAD_PHQ1)
## 
## Call:
## lm(formula = GADz ~ PHQz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6373 -0.4369 -0.0781  0.3234  1.8507 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.062087   0.278991  -0.223  0.82412    
## PHQz                          0.741169   0.047598  15.571  < 2e-16 ***
## n_disability1                 0.145640   0.135134   1.078  0.28243    
## student.status1              -0.062110   0.118024  -0.526  0.59929    
## student.status2               0.048002   0.117273   0.409  0.68274    
## n_orientationSexual Minority  0.148096   0.105298   1.406  0.16112    
## n_ethnicity1                 -0.089363   0.103369  -0.864  0.38833    
## n_gender1                    -0.542061   0.361393  -1.500  0.13519    
## n_gender2                     0.082073   0.136743   0.600  0.54904    
## UGPG1                         0.058371   0.116840   0.500  0.61791    
## n_yearYear 2                 -0.191125   0.133622  -1.430  0.15416    
## n_yearYear 3                 -0.381503   0.126895  -3.006  0.00298 ** 
## n_yearYear 4+                -0.452291   0.167368  -2.702  0.00747 ** 
## age                           0.003812   0.011800   0.323  0.74698    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6335 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.6358, Adjusted R-squared:  0.6124 
## F-statistic: 27.26 on 13 and 203 DF,  p-value: < 2.2e-16
summary(GAD_CUDIT1)
## 
## Call:
## lm(formula = GADz ~ CUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5938 -0.5578 -0.1419  0.4542  2.3902 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                  -0.3331328  0.6648602  -0.501   0.6176  
## CUDITz                        0.0861680  0.1023437   0.842   0.4021  
## n_disability1                 0.6545319  0.2506095   2.612   0.0106 *
## student.status1              -0.5408017  0.2347174  -2.304   0.0236 *
## student.status2               0.0417510  0.2642718   0.158   0.8748  
## n_orientationSexual Minority  0.2684025  0.2103868   1.276   0.2054  
## n_ethnicity1                  0.0002369  0.2243371   0.001   0.9992  
## n_gender1                     1.1776510  1.0101448   1.166   0.2469  
## n_gender2                     0.3916857  0.2919485   1.342   0.1832  
## UGPG1                         0.0494372  0.2601301   0.190   0.8497  
## n_yearYear 2                 -0.2678335  0.2749213  -0.974   0.3326  
## n_yearYear 3                 -0.4626350  0.2786039  -1.661   0.1004  
## n_yearYear 4+                -0.6078504  0.3482722  -1.745   0.0845 .
## age                           0.0018698  0.0278513   0.067   0.9466  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9052 on 87 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.2822, Adjusted R-squared:  0.175 
## F-statistic: 2.632 on 13 and 87 DF,  p-value: 0.003857
summary(GAD_AUDIT1)
## 
## Call:
## lm(formula = GADz ~ AUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7823 -0.6214 -0.1347  0.5685  2.4613 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.318337   0.412190   0.772  0.44083    
## AUDITz                        0.110235   0.065338   1.687  0.09311 .  
## n_disability1                 0.781297   0.189740   4.118 5.57e-05 ***
## student.status1              -0.301289   0.172107  -1.751  0.08153 .  
## student.status2              -0.043440   0.172523  -0.252  0.80145    
## n_orientationSexual Minority  0.291187   0.154545   1.884  0.06097 .  
## n_ethnicity1                 -0.009085   0.152307  -0.060  0.95249    
## n_gender1                    -0.400260   0.531551  -0.753  0.45232    
## n_gender2                     0.142690   0.202046   0.706  0.48086    
## UGPG1                         0.009722   0.172576   0.056  0.95513    
## n_yearYear 2                 -0.367162   0.196262  -1.871  0.06282 .  
## n_yearYear 3                 -0.415291   0.187443  -2.216  0.02783 *  
## n_yearYear 4+                -0.730237   0.245378  -2.976  0.00328 ** 
## age                          -0.018440   0.017330  -1.064  0.28855    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.932 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2118, Adjusted R-squared:  0.1613 
## F-statistic: 4.195 on 13 and 203 DF,  p-value: 3.523e-06
summary(GAD_unil1)
## 
## Call:
## lm(formula = GADz ~ unil_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7452 -0.5870 -0.1324  0.4621  2.3642 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.33064    0.37489   0.882 0.378847    
## unil_z                        0.39570    0.06179   6.404 1.03e-09 ***
## n_disability1                 0.62146    0.17587   3.534 0.000507 ***
## student.status1              -0.29100    0.15805  -1.841 0.067061 .  
## student.status2              -0.12411    0.15848  -0.783 0.434460    
## n_orientationSexual Minority  0.22510    0.14220   1.583 0.114974    
## n_ethnicity1                 -0.03400    0.13956  -0.244 0.807792    
## n_gender1                    -0.27685    0.48835  -0.567 0.571405    
## n_gender2                     0.23475    0.18576   1.264 0.207766    
## UGPG1                         0.10603    0.15823   0.670 0.503560    
## n_yearYear 2                 -0.36223    0.18003  -2.012 0.045528 *  
## n_yearYear 3                 -0.30860    0.17188  -1.795 0.074076 .  
## n_yearYear 4+                -0.59123    0.22578  -2.619 0.009495 ** 
## age                          -0.02233    0.01578  -1.415 0.158591    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.856 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.335,  Adjusted R-squared:  0.2925 
## F-statistic: 7.868 on 13 and 203 DF,  p-value: 1.26e-12
summary(GAD_prel1)
## 
## Call:
## lm(formula = GADz ~ prel_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9478 -0.6332 -0.1516  0.4744  2.7261 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.42354    0.39036   1.085  0.27920    
## prel_z                        0.32944    0.07061   4.666 5.58e-06 ***
## n_disability1                 0.58173    0.18619   3.124  0.00204 ** 
## student.status1              -0.27282    0.16483  -1.655  0.09944 .  
## student.status2              -0.08145    0.16486  -0.494  0.62179    
## n_orientationSexual Minority  0.14976    0.15146   0.989  0.32395    
## n_ethnicity1                 -0.09383    0.14612  -0.642  0.52150    
## n_gender1                    -0.61714    0.51109  -1.208  0.22864    
## n_gender2                     0.22522    0.19405   1.161  0.24716    
## UGPG1                         0.05461    0.16452   0.332  0.74028    
## n_yearYear 2                 -0.29216    0.18797  -1.554  0.12168    
## n_yearYear 3                 -0.29346    0.17975  -1.633  0.10411    
## n_yearYear 4+                -0.57874    0.23611  -2.451  0.01509 *  
## age                          -0.02383    0.01645  -1.449  0.14891    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8919 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2781, Adjusted R-squared:  0.2319 
## F-statistic: 6.016 on 13 and 203 DF,  p-value: 1.962e-09
summary(GAD_SA1)
## 
## Call:
## lm(formula = GADz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0116 -0.5341 -0.1172  0.4929  2.2654 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.058302   0.352501   0.165 0.868798    
## SAz                           0.504053   0.057649   8.743 8.61e-16 ***
## n_disability1                 0.577400   0.164370   3.513 0.000546 ***
## student.status1              -0.173475   0.148474  -1.168 0.244019    
## student.status2              -0.050801   0.147807  -0.344 0.731431    
## n_orientationSexual Minority  0.074618   0.135008   0.553 0.581078    
## n_ethnicity1                 -0.036331   0.130417  -0.279 0.780853    
## n_gender1                    -0.440174   0.456148  -0.965 0.335704    
## n_gender2                     0.137681   0.172661   0.797 0.426147    
## UGPG1                        -0.015660   0.147627  -0.106 0.915628    
## n_yearYear 2                 -0.380055   0.168251  -2.259 0.024955 *  
## n_yearYear 3                 -0.202889   0.161586  -1.256 0.210701    
## n_yearYear 4+                -0.418296   0.212889  -1.965 0.050796 .  
## age                          -0.005225   0.014876  -0.351 0.725776    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7999 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4194, Adjusted R-squared:  0.3822 
## F-statistic: 11.28 on 13 and 203 DF,  p-value: < 2.2e-16
summary(GAD_SCI1)
## 
## Call:
## lm(formula = GADz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9883 -0.4826 -0.1459  0.4497  2.4909 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.08507    0.34289   0.248  0.80431    
## SCInz                         0.55051    0.05886   9.353  < 2e-16 ***
## n_disability1                 0.46374    0.16329   2.840  0.00498 ** 
## student.status1              -0.19087    0.14610  -1.306  0.19292    
## student.status2               0.03684    0.14598   0.252  0.80105    
## n_orientationSexual Minority  0.14965    0.13139   1.139  0.25608    
## n_ethnicity1                 -0.04845    0.12815  -0.378  0.70579    
## n_gender1                    -0.18030    0.44323  -0.407  0.68461    
## n_gender2                     0.30873    0.16905   1.826  0.06931 .  
## UGPG1                         0.06624    0.14383   0.461  0.64563    
## n_yearYear 2                 -0.27225    0.16606  -1.639  0.10270    
## n_yearYear 3                 -0.28526    0.15631  -1.825  0.06951 .  
## n_yearYear 4+                -0.54294    0.20506  -2.648  0.00876 ** 
## age                          -0.01454    0.01438  -1.011  0.31317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7762 on 198 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.4496, Adjusted R-squared:  0.4134 
## F-statistic: 12.44 on 13 and 198 DF,  p-value: < 2.2e-16
summary(GAD_PS1)
## 
## Call:
## lm(formula = GADz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.02602 -0.47539 -0.07708  0.38329  2.30079 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.189244   0.314393   0.602  0.54790    
## PSz                           0.660185   0.054769  12.054  < 2e-16 ***
## n_disability1                 0.301669   0.151225   1.995  0.04742 *  
## student.status1              -0.024779   0.134858  -0.184  0.85440    
## student.status2               0.007760   0.132757   0.058  0.95345    
## n_orientationSexual Minority  0.168852   0.119161   1.417  0.15804    
## n_ethnicity1                 -0.148914   0.117706  -1.265  0.20729    
## n_gender1                    -0.826069   0.409770  -2.016  0.04515 *  
## n_gender2                     0.093213   0.154485   0.603  0.54694    
## UGPG1                         0.080734   0.132310   0.610  0.54243    
## n_yearYear 2                 -0.216686   0.153308  -1.413  0.15909    
## n_yearYear 3                 -0.435242   0.143576  -3.031  0.00276 ** 
## n_yearYear 4+                -0.508501   0.188911  -2.692  0.00771 ** 
## age                          -0.007872   0.013258  -0.594  0.55333    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7154 on 200 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5394, Adjusted R-squared:  0.5094 
## F-statistic: 18.01 on 13 and 200 DF,  p-value: < 2.2e-16
summary(GAD_wellbeing1)
## 
## Call:
## lm(formula = GADz ~ wellbeingz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.48007 -0.50586 -0.09251  0.46304  2.34722 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.286658   0.322270  -0.889   0.3748    
## wellbeingz                   -0.636459   0.054071 -11.771   <2e-16 ***
## n_disability1                 0.369275   0.151433   2.439   0.0156 *  
## student.status1               0.033715   0.137155   0.246   0.8061    
## student.status2               0.001111   0.134057   0.008   0.9934    
## n_orientationSexual Minority  0.215882   0.120019   1.799   0.0736 .  
## n_ethnicity1                 -0.006985   0.118274  -0.059   0.9530    
## n_gender1                    -0.327677   0.412129  -0.795   0.4275    
## n_gender2                     0.255338   0.156538   1.631   0.1044    
## UGPG1                         0.010948   0.133557   0.082   0.9348    
## n_yearYear 2                 -0.334751   0.154175  -2.171   0.0311 *  
## n_yearYear 3                 -0.343674   0.145010  -2.370   0.0187 *  
## n_yearYear 4+                -0.444043   0.191365  -2.320   0.0213 *  
## age                           0.003484   0.013507   0.258   0.7967    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7225 on 200 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5302, Adjusted R-squared:  0.4996 
## F-statistic: 17.36 on 13 and 200 DF,  p-value: < 2.2e-16
summary(GAD_SC1)
## 
## Call:
## lm(formula = GADz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9119 -0.6703 -0.1281  0.5403  2.4950 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.39984    0.41002   0.975  0.33063    
## SCz                          -0.06743    0.06411  -1.052  0.29414    
## n_disability1                 0.77149    0.19053   4.049 7.31e-05 ***
## student.status1              -0.30926    0.17279  -1.790  0.07498 .  
## student.status2              -0.07413    0.17340  -0.428  0.66944    
## n_orientationSexual Minority  0.29280    0.15566   1.881  0.06139 .  
## n_ethnicity1                 -0.03163    0.15267  -0.207  0.83606    
## n_gender1                    -0.36240    0.53394  -0.679  0.49809    
## n_gender2                     0.11469    0.20204   0.568  0.57089    
## UGPG1                         0.03571    0.17260   0.207  0.83628    
## n_yearYear 2                 -0.34900    0.19683  -1.773  0.07771 .  
## n_yearYear 3                 -0.38294    0.18750  -2.042  0.04241 *  
## n_yearYear 4+                -0.69899    0.24621  -2.839  0.00499 ** 
## age                          -0.02111    0.01729  -1.221  0.22353    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9359 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.205,  Adjusted R-squared:  0.1541 
## F-statistic: 4.027 on 13 and 203 DF,  p-value: 7.051e-06
summary(GAD_perfectionism1)
## 
## Call:
## lm(formula = GADz ~ perfectionismz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7150 -0.6102 -0.1922  0.5538  2.4532 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.16621    0.37974   0.438 0.662082    
## perfectionismz                0.36027    0.06077   5.928 1.34e-08 ***
## n_disability1                 0.60634    0.17935   3.381 0.000871 ***
## student.status1              -0.31490    0.16083  -1.958 0.051644 .  
## student.status2              -0.03538    0.16119  -0.219 0.826514    
## n_orientationSexual Minority  0.21175    0.14503   1.460 0.145849    
## n_ethnicity1                 -0.02405    0.14175  -0.170 0.865438    
## n_gender1                    -0.08439    0.49231  -0.171 0.864070    
## n_gender2                     0.19172    0.18608   1.030 0.304119    
## UGPG1                         0.09025    0.15928   0.567 0.571625    
## n_yearYear 2                 -0.36529    0.18332  -1.993 0.047678 *  
## n_yearYear 3                 -0.30202    0.17306  -1.745 0.082498 .  
## n_yearYear 4+                -0.59767    0.22675  -2.636 0.009059 ** 
## age                          -0.01473    0.01594  -0.924 0.356593    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.859 on 198 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.326,  Adjusted R-squared:  0.2817 
## F-statistic: 7.367 on 13 and 198 DF,  p-value: 1.012e-11
summary(GAD_aca1)
## 
## Call:
## lm(formula = GADz ~ SE_aca_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2177 -0.6026 -0.1280  0.5096  2.4592 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.205578   0.377403   0.545  0.58654    
## SE_aca_z                     -0.423369   0.067436  -6.278 2.04e-09 ***
## n_disability1                 0.563157   0.178027   3.163  0.00180 ** 
## student.status1              -0.242980   0.158891  -1.529  0.12777    
## student.status2              -0.116708   0.158939  -0.734  0.46361    
## n_orientationSexual Minority  0.297639   0.142058   2.095  0.03739 *  
## n_ethnicity1                 -0.328817   0.148058  -2.221  0.02747 *  
## n_gender1                    -0.292910   0.489894  -0.598  0.55057    
## n_gender2                     0.226724   0.186273   1.217  0.22496    
## UGPG1                         0.112643   0.158844   0.709  0.47905    
## n_yearYear 2                 -0.388824   0.180715  -2.152  0.03261 *  
## n_yearYear 3                 -0.455061   0.172361  -2.640  0.00893 ** 
## n_yearYear 4+                -0.685672   0.225815  -3.036  0.00271 ** 
## age                          -0.009794   0.015958  -0.614  0.54008    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8588 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3307, Adjusted R-squared:  0.2878 
## F-statistic: 7.714 on 13 and 203 DF,  p-value: 2.291e-12
summary(GAD_fi1)
## 
## Call:
## lm(formula = GADz ~ SE_fi_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9220 -0.6448 -0.1841  0.5871  2.4644 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.39026    0.39361   0.991 0.322625    
## SE_fi_z                       0.27311    0.06415   4.257 3.16e-05 ***
## n_disability1                 0.72108    0.18346   3.930 0.000116 ***
## student.status1              -0.26719    0.16629  -1.607 0.109658    
## student.status2              -0.08126    0.16622  -0.489 0.625457    
## n_orientationSexual Minority  0.23042    0.14989   1.537 0.125786    
## n_ethnicity1                 -0.01556    0.14663  -0.106 0.915606    
## n_gender1                    -0.25896    0.51352  -0.504 0.614601    
## n_gender2                     0.21752    0.19569   1.112 0.267649    
## UGPG1                         0.15799    0.16828   0.939 0.348926    
## n_yearYear 2                 -0.34324    0.18911  -1.815 0.070997 .  
## n_yearYear 3                 -0.29218    0.18147  -1.610 0.108932    
## n_yearYear 4+                -0.56042    0.23893  -2.346 0.019966 *  
## age                          -0.02722    0.01662  -1.637 0.103087    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8992 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2662, Adjusted R-squared:  0.2192 
## F-statistic: 5.665 on 13 and 203 DF,  p-value: 8.174e-09
summary(GAD_acc1)
## 
## Call:
## lm(formula = GADz ~ SE_acc_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2177 -0.6026 -0.1280  0.5096  2.4592 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.205578   0.377403   0.545  0.58654    
## SE_acc_z                     -0.423369   0.067436  -6.278 2.04e-09 ***
## n_disability1                 0.563157   0.178027   3.163  0.00180 ** 
## student.status1              -0.242980   0.158891  -1.529  0.12777    
## student.status2              -0.116708   0.158939  -0.734  0.46361    
## n_orientationSexual Minority  0.297639   0.142058   2.095  0.03739 *  
## n_ethnicity1                 -0.328817   0.148058  -2.221  0.02747 *  
## n_gender1                    -0.292910   0.489894  -0.598  0.55057    
## n_gender2                     0.226724   0.186273   1.217  0.22496    
## UGPG1                         0.112643   0.158844   0.709  0.47905    
## n_yearYear 2                 -0.388824   0.180715  -2.152  0.03261 *  
## n_yearYear 3                 -0.455061   0.172361  -2.640  0.00893 ** 
## n_yearYear 4+                -0.685672   0.225815  -3.036  0.00271 ** 
## age                          -0.009794   0.015958  -0.614  0.54008    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8588 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3307, Adjusted R-squared:  0.2878 
## F-statistic: 7.714 on 13 and 203 DF,  p-value: 2.291e-12
summary(GAD_fr1)
## 
## Call:
## lm(formula = GADz ~ SE_fr_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7856 -0.6681 -0.1259  0.5154  2.6518 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.26100    0.39769   0.656 0.512378    
## SE_fr_z                      -0.25159    0.06344  -3.966 0.000101 ***
## n_disability1                 0.80588    0.18420   4.375 1.94e-05 ***
## student.status1              -0.36266    0.16748  -2.165 0.031527 *  
## student.status2              -0.14095    0.16828  -0.838 0.403241    
## n_orientationSexual Minority  0.25185    0.15025   1.676 0.095245 .  
## n_ethnicity1                 -0.02650    0.14741  -0.180 0.857523    
## n_gender1                    -0.30520    0.51588  -0.592 0.554767    
## n_gender2                     0.21673    0.19696   1.100 0.272474    
## UGPG1                        -0.01436    0.16721  -0.086 0.931654    
## n_yearYear 2                 -0.36669    0.19018  -1.928 0.055239 .  
## n_yearYear 3                 -0.34967    0.18133  -1.928 0.055206 .  
## n_yearYear 4+                -0.61414    0.23887  -2.571 0.010857 *  
## age                          -0.01725    0.01672  -1.032 0.303283    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9041 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2582, Adjusted R-squared:  0.2107 
## F-statistic: 5.434 on 13 and 203 DF,  p-value: 2.104e-08
summary(GAD_co1)
## 
## Call:
## lm(formula = GADz ~ SE_co_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7810 -0.6445 -0.1567  0.5826  2.5974 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.562149   0.404424   1.390  0.16611    
## SE_co_z                      -0.219827   0.067972  -3.234  0.00143 ** 
## n_disability1                 0.788278   0.192184   4.102 6.02e-05 ***
## student.status1              -0.185845   0.173375  -1.072  0.28508    
## student.status2              -0.002696   0.177510  -0.015  0.98790    
## n_orientationSexual Minority  0.244516   0.155168   1.576  0.11669    
## n_ethnicity1                 -0.009500   0.152633  -0.062  0.95044    
## n_gender1                    -0.465235   0.522463  -0.890  0.37431    
## n_gender2                     0.124265   0.200871   0.619  0.53688    
## UGPG1                        -0.021473   0.170189  -0.126  0.89973    
## n_yearYear 2                 -0.298962   0.192421  -1.554  0.12188    
## n_yearYear 3                 -0.351289   0.192908  -1.821  0.07014 .  
## n_yearYear 4+                -0.591471   0.241655  -2.448  0.01527 *  
## age                          -0.030940   0.016993  -1.821  0.07017 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9106 on 195 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.2452, Adjusted R-squared:  0.1949 
## F-statistic: 4.874 on 13 and 195 DF,  p-value: 2.325e-07
summary(GAD_PHQ2)
## 
## Call:
## lm(formula = GADz ~ PHQz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.59261 -0.43057 -0.09267  0.32982  1.88427 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.057924   0.279480  -0.207  0.83602    
## PHQz                          0.729875   0.050884  14.344  < 2e-16 ***
## n_disability1                 0.149203   0.135450   1.102  0.27197    
## student.status1              -0.061263   0.118205  -0.518  0.60483    
## student.status2               0.044063   0.117610   0.375  0.70831    
## n_orientationSexual Minority  0.141638   0.105944   1.337  0.18275    
## n_ethnicity1                 -0.087157   0.103580  -0.841  0.40110    
## n_gender1                    -0.525996   0.362811  -1.450  0.14867    
## n_gender2                     0.094520   0.138343   0.683  0.49525    
## UGPG1                         0.071715   0.118887   0.603  0.54704    
## n_yearYear 2                 -0.192796   0.133845  -1.440  0.15129    
## n_yearYear 3                 -0.370980   0.128159  -2.895  0.00421 ** 
## n_yearYear 4+                -0.439623   0.168800  -2.604  0.00989 ** 
## age                           0.002856   0.011913   0.240  0.81080    
## SE_fi_z                       0.030654   0.048315   0.634  0.52650    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6345 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.6365, Adjusted R-squared:  0.6113 
## F-statistic: 25.26 on 14 and 202 DF,  p-value: < 2.2e-16
summary(GAD_CUDIT2)
## 
## Call:
## lm(formula = GADz ~ CUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5465 -0.5419 -0.1571  0.4518  2.0349 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.376827   0.586265  -0.643   0.5221    
## CUDITz                        0.147781   0.091044   1.623   0.1082    
## n_disability1                 0.579415   0.221453   2.616   0.0105 *  
## student.status1              -0.454210   0.207646  -2.187   0.0314 *  
## student.status2               0.091761   0.233213   0.393   0.6950    
## n_orientationSexual Minority  0.097669   0.188504   0.518   0.6057    
## n_ethnicity1                  0.082860   0.198461   0.418   0.6773    
## n_gender1                     1.634941   0.895155   1.826   0.0713 .  
## n_gender2                     0.588013   0.260282   2.259   0.0264 *  
## UGPG1                         0.464066   0.243388   1.907   0.0599 .  
## n_yearYear 2                 -0.157400   0.243365  -0.647   0.5195    
## n_yearYear 3                 -0.252540   0.249086  -1.014   0.3135    
## n_yearYear 4+                -0.001221   0.329381  -0.004   0.9971    
## age                          -0.017531   0.024850  -0.705   0.4824    
## SE_fi_z                       0.434126   0.085280   5.091 2.08e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7981 on 86 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.4484, Adjusted R-squared:  0.3586 
## F-statistic: 4.994 on 14 and 86 DF,  p-value: 1.05e-06
summary(GAD_AUDIT2)
## 
## Call:
## lm(formula = GADz ~ AUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9292 -0.6204 -0.1644  0.5039  2.4298 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.3031662  0.3963907   0.765   0.4453    
## AUDITz                        0.0965253  0.0629163   1.534   0.1265    
## n_disability1                 0.7278195  0.1829063   3.979 9.64e-05 ***
## student.status1              -0.2619633  0.1657700  -1.580   0.1156    
## student.status2              -0.0655438  0.1659874  -0.395   0.6934    
## n_orientationSexual Minority  0.2155034  0.1497114   1.439   0.1516    
## n_ethnicity1                 -0.0003388  0.1464783  -0.002   0.9982    
## n_gender1                    -0.2788774  0.5119777  -0.545   0.5866    
## n_gender2                     0.2437188  0.1957868   1.245   0.2146    
## UGPG1                         0.1326279  0.1685319   0.787   0.4322    
## n_yearYear 2                 -0.3584669  0.1887427  -1.899   0.0590 .  
## n_yearYear 3                 -0.3190448  0.1817110  -1.756   0.0806 .  
## n_yearYear 4+                -0.5826551  0.2385820  -2.442   0.0155 *  
## age                          -0.0238049  0.0167142  -1.424   0.1559    
## SE_fi_z                       0.2679927  0.0640210   4.186 4.24e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8962 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2747, Adjusted R-squared:  0.2244 
## F-statistic: 5.464 on 14 and 202 DF,  p-value: 7.498e-09
summary(GAD_unil2)
## 
## Call:
## lm(formula = GADz ~ unil_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6764 -0.5683 -0.1058  0.4843  2.4818 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.31394    0.36316   0.864 0.388354    
## unil_z                        0.36594    0.06036   6.062 6.48e-09 ***
## n_disability1                 0.58857    0.17058   3.450 0.000681 ***
## student.status1              -0.25840    0.15334  -1.685 0.093503 .  
## student.status2              -0.13617    0.15354  -0.887 0.376218    
## n_orientationSexual Minority  0.16567    0.13863   1.195 0.233443    
## n_ethnicity1                 -0.02422    0.13521  -0.179 0.858002    
## n_gender1                    -0.18435    0.47367  -0.389 0.697547    
## n_gender2                     0.31400    0.18114   1.733 0.084544 .  
## UGPG1                         0.20167    0.15533   1.298 0.195659    
## n_yearYear 2                 -0.35578    0.17439  -2.040 0.042633 *  
## n_yearYear 3                 -0.23629    0.16758  -1.410 0.160073    
## n_yearYear 4+                -0.47786    0.22074  -2.165 0.031573 *  
## age                          -0.02645    0.01533  -1.726 0.085885 .  
## SE_fi_z                       0.22605    0.05966   3.789 0.000199 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8291 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3792, Adjusted R-squared:  0.3361 
## F-statistic: 8.812 on 14 and 202 DF,  p-value: 7.296e-15
summary(GAD_prel2)
## 
## Call:
## lm(formula = GADz ~ prel_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1053 -0.5867 -0.2036  0.5197  2.4036 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.39490    0.37240   1.060  0.29023    
## prel_z                        0.33534    0.06737   4.978 1.37e-06 ***
## n_disability1                 0.52348    0.17806   2.940  0.00367 ** 
## student.status1              -0.23038    0.15750  -1.463  0.14510    
## student.status2              -0.10255    0.15732  -0.652  0.51524    
## n_orientationSexual Minority  0.06571    0.14563   0.451  0.65232    
## n_ethnicity1                 -0.08365    0.13940  -0.600  0.54911    
## n_gender1                    -0.49769    0.48821  -1.019  0.30923    
## n_gender2                     0.33657    0.18668   1.803  0.07289 .  
## UGPG1                         0.17943    0.15927   1.127  0.26127    
## n_yearYear 2                 -0.28431    0.17931  -1.586  0.11440    
## n_yearYear 3                 -0.19535    0.17279  -1.131  0.25957    
## n_yearYear 4+                -0.42574    0.22767  -1.870  0.06294 .  
## age                          -0.02895    0.01573  -1.841  0.06714 .  
## SE_fi_z                       0.27887    0.06070   4.594 7.64e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8507 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3464, Adjusted R-squared:  0.3011 
## F-statistic: 7.647 on 14 and 202 DF,  p-value: 7.784e-13
summary(GAD_SA2)
## 
## Call:
## lm(formula = GADz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.93479 -0.51064 -0.09116  0.50449  2.48198 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.061128   0.343106   0.178 0.858774    
## SAz                           0.471655   0.056870   8.294 1.53e-14 ***
## n_disability1                 0.551336   0.160162   3.442 0.000701 ***
## student.status1              -0.152570   0.144639  -1.055 0.292763    
## student.status2              -0.066113   0.143933  -0.459 0.646488    
## n_orientationSexual Minority  0.032343   0.131962   0.245 0.806636    
## n_ethnicity1                 -0.027665   0.126965  -0.218 0.827732    
## n_gender1                    -0.348848   0.444755  -0.784 0.433748    
## n_gender2                     0.213214   0.169437   1.258 0.209711    
## UGPG1                         0.075684   0.146039   0.518 0.604853    
## n_yearYear 2                 -0.373299   0.163778  -2.279 0.023694 *  
## n_yearYear 3                 -0.146507   0.158100  -0.927 0.355204    
## n_yearYear 4+                -0.330365   0.208730  -1.583 0.115045    
## age                          -0.009918   0.014541  -0.682 0.495976    
## SE_fi_z                       0.197180   0.056290   3.503 0.000567 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7786 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4526, Adjusted R-squared:  0.4147 
## F-statistic: 11.93 on 14 and 202 DF,  p-value: < 2.2e-16
summary(GAD_SCI2)
## 
## Call:
## lm(formula = GADz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0479 -0.4868 -0.1691  0.5064  2.3949 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.08881    0.33922   0.262  0.79375    
## SCInz                         0.51257    0.06051   8.470 5.62e-15 ***
## n_disability1                 0.46288    0.16155   2.865  0.00462 ** 
## student.status1              -0.17688    0.14466  -1.223  0.22289    
## student.status2               0.01127    0.14484   0.078  0.93804    
## n_orientationSexual Minority  0.11677    0.13076   0.893  0.37296    
## n_ethnicity1                 -0.03836    0.12685  -0.302  0.76265    
## n_gender1                    -0.13685    0.43889  -0.312  0.75552    
## n_gender2                     0.34680    0.16805   2.064  0.04036 *  
## UGPG1                         0.12751    0.14475   0.881  0.37947    
## n_yearYear 2                 -0.27336    0.16428  -1.664  0.09772 .  
## n_yearYear 3                 -0.24270    0.15573  -1.558  0.12073    
## n_yearYear 4+                -0.47915    0.20474  -2.340  0.02027 *  
## age                          -0.01737    0.01428  -1.217  0.22512    
## SE_fi_z                       0.13324    0.05782   2.304  0.02224 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7679 on 197 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.464,  Adjusted R-squared:  0.4259 
## F-statistic: 12.18 on 14 and 197 DF,  p-value: < 2.2e-16
summary(GAD_PS2)
## 
## Call:
## lm(formula = GADz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1867 -0.4492 -0.1101  0.3478  2.2327 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.187779   0.312312   0.601  0.54835    
## PSz                           0.629615   0.056695  11.105  < 2e-16 ***
## n_disability1                 0.303592   0.150227   2.021  0.04463 *  
## student.status1              -0.021404   0.133976  -0.160  0.87324    
## student.status2              -0.005262   0.132053  -0.040  0.96825    
## n_orientationSexual Minority  0.146171   0.118962   1.229  0.22063    
## n_ethnicity1                 -0.140169   0.117015  -1.198  0.23239    
## n_gender1                    -0.761778   0.408436  -1.865  0.06364 .  
## n_gender2                     0.133775   0.154913   0.864  0.38887    
## UGPG1                         0.125505   0.133492   0.940  0.34827    
## n_yearYear 2                 -0.219829   0.152302  -1.443  0.15049    
## n_yearYear 3                 -0.396849   0.144024  -2.755  0.00641 ** 
## n_yearYear 4+                -0.462270   0.189203  -2.443  0.01543 *  
## age                          -0.010346   0.013233  -0.782  0.43526    
## SE_fi_z                       0.102170   0.053292   1.917  0.05665 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7107 on 199 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5477, Adjusted R-squared:  0.5159 
## F-statistic: 17.21 on 14 and 199 DF,  p-value: < 2.2e-16
summary(GAD_wellbeing2)
## 
## Call:
## lm(formula = GADz ~ wellbeingz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66010 -0.51977 -0.09716  0.42298  2.22061 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.2652412  0.3205988  -0.827   0.4090    
## wellbeingz                   -0.6065122  0.0561837 -10.795   <2e-16 ***
## n_disability1                 0.3690117  0.1505479   2.451   0.0151 *  
## student.status1               0.0336561  0.1363530   0.247   0.8053    
## student.status2              -0.0113196  0.1334458  -0.085   0.9325    
## n_orientationSexual Minority  0.1920036  0.1200263   1.600   0.1113    
## n_ethnicity1                 -0.0048784  0.1175884  -0.041   0.9669    
## n_gender1                    -0.2878429  0.4102965  -0.702   0.4838    
## n_gender2                     0.2870425  0.1565817   1.833   0.0683 .  
## UGPG1                         0.0575029  0.1351848   0.425   0.6710    
## n_yearYear 2                 -0.3326370  0.1532777  -2.170   0.0312 *  
## n_yearYear 3                 -0.3106824  0.1452818  -2.138   0.0337 *  
## n_yearYear 4+                -0.4027059  0.1915792  -2.102   0.0368 *  
## age                           0.0005229  0.0135251   0.039   0.9692    
## SE_fi_z                       0.0990102  0.0540240   1.833   0.0683 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7183 on 199 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.538,  Adjusted R-squared:  0.5055 
## F-statistic: 16.55 on 14 and 199 DF,  p-value: < 2.2e-16
summary(GAD_SC2)
## 
## Call:
## lm(formula = GADz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9551 -0.6347 -0.1874  0.5751  2.4798 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.38601    0.39484   0.978 0.329428    
## SCz                          -0.01674    0.06296  -0.266 0.790600    
## n_disability1                 0.72094    0.18388   3.921 0.000121 ***
## student.status1              -0.26798    0.16670  -1.608 0.109487    
## student.status2              -0.08428    0.16699  -0.505 0.614322    
## n_orientationSexual Minority  0.22717    0.15074   1.507 0.133358    
## n_ethnicity1                 -0.01692    0.14705  -0.115 0.908511    
## n_gender1                    -0.25608    0.51481  -0.497 0.619427    
## n_gender2                     0.21723    0.19614   1.107 0.269401    
## UGPG1                         0.15637    0.16877   0.927 0.355277    
## n_yearYear 2                 -0.34309    0.18954  -1.810 0.071767 .  
## n_yearYear 3                 -0.29244    0.18189  -1.608 0.109435    
## n_yearYear 4+                -0.56003    0.23949  -2.338 0.020342 *  
## age                          -0.02688    0.01671  -1.609 0.109281    
## SE_fi_z                       0.26969    0.06556   4.114 5.67e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9013 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2665, Adjusted R-squared:  0.2156 
## F-statistic: 5.242 on 14 and 202 DF,  p-value: 1.959e-08
summary(GAD_perfectionism2)
## 
## Call:
## lm(formula = GADz ~ perfectionismz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5630 -0.6260 -0.1823  0.5574  2.3054 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.16073    0.37003   0.434 0.664494    
## perfectionismz                0.32503    0.06012   5.406 1.85e-07 ***
## n_disability1                 0.58739    0.17485   3.359 0.000938 ***
## student.status1              -0.27938    0.15707  -1.779 0.076833 .  
## student.status2              -0.06743    0.15735  -0.429 0.668720    
## n_orientationSexual Minority  0.15295    0.14238   1.074 0.284025    
## n_ethnicity1                 -0.01116    0.13817  -0.081 0.935734    
## n_gender1                    -0.02364    0.48006  -0.049 0.960776    
## n_gender2                     0.26501    0.18260   1.451 0.148282    
## UGPG1                         0.18419    0.15766   1.168 0.244104    
## n_yearYear 2                 -0.35680    0.17865  -1.997 0.047184 *  
## n_yearYear 3                 -0.23272    0.16986  -1.370 0.172215    
## n_yearYear 4+                -0.49081    0.22318  -2.199 0.029031 *  
## age                          -0.01907    0.01558  -1.224 0.222474    
## SE_fi_z                       0.20906    0.06157   3.396 0.000828 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.837 on 197 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.3633, Adjusted R-squared:  0.318 
## F-statistic: 8.028 on 14 and 197 DF,  p-value: 1.963e-13
summary(GAD_aca2)
## 
## Call:
## lm(formula = GADz ~ SE_aca_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2584 -0.5796 -0.1589  0.5452  2.3255 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.21496    0.37093   0.580  0.56289    
## SE_aca_z                     -0.36797    0.06906  -5.329 2.63e-07 ***
## n_disability1                 0.55562    0.17499   3.175  0.00173 ** 
## student.status1              -0.22464    0.15629  -1.437  0.15219    
## student.status2              -0.12274    0.15622  -0.786  0.43298    
## n_orientationSexual Minority  0.24707    0.14073   1.756  0.08067 .  
## n_ethnicity1                 -0.28197    0.14643  -1.926  0.05556 .  
## n_gender1                    -0.22482    0.48206  -0.466  0.64146    
## n_gender2                     0.28176    0.18408   1.531  0.12743    
## UGPG1                         0.18263    0.15802   1.156  0.24917    
## n_yearYear 2                 -0.37935    0.17764  -2.136  0.03392 *  
## n_yearYear 3                 -0.38412    0.17121  -2.244  0.02594 *  
## n_yearYear 4+                -0.59174    0.22436  -2.638  0.00900 ** 
## age                          -0.01469    0.01578  -0.931  0.35280    
## SE_fi_z                       0.17923    0.06274   2.857  0.00473 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.844 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3567, Adjusted R-squared:  0.3121 
## F-statistic: 7.999 on 14 and 202 DF,  p-value: 1.868e-13
summary(GAD_acc2)
## 
## Call:
## lm(formula = GADz ~ SE_acc_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2584 -0.5796 -0.1589  0.5452  2.3255 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.21496    0.37093   0.580  0.56289    
## SE_acc_z                     -0.36797    0.06906  -5.329 2.63e-07 ***
## n_disability1                 0.55562    0.17499   3.175  0.00173 ** 
## student.status1              -0.22464    0.15629  -1.437  0.15219    
## student.status2              -0.12274    0.15622  -0.786  0.43298    
## n_orientationSexual Minority  0.24707    0.14073   1.756  0.08067 .  
## n_ethnicity1                 -0.28197    0.14643  -1.926  0.05556 .  
## n_gender1                    -0.22482    0.48206  -0.466  0.64146    
## n_gender2                     0.28176    0.18408   1.531  0.12743    
## UGPG1                         0.18263    0.15802   1.156  0.24917    
## n_yearYear 2                 -0.37935    0.17764  -2.136  0.03392 *  
## n_yearYear 3                 -0.38412    0.17121  -2.244  0.02594 *  
## n_yearYear 4+                -0.59174    0.22436  -2.638  0.00900 ** 
## age                          -0.01469    0.01578  -0.931  0.35280    
## SE_fi_z                       0.17923    0.06274   2.857  0.00473 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.844 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3567, Adjusted R-squared:  0.3121 
## F-statistic: 7.999 on 14 and 202 DF,  p-value: 1.868e-13
summary(GAD_fr2)
## 
## Call:
## lm(formula = GADz ~ SE_fr_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7184 -0.5923 -0.1415  0.5432  2.4337 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.24121    0.38217   0.631 0.528646    
## SE_fr_z                      -0.24004    0.06102  -3.934 0.000115 ***
## n_disability1                 0.75293    0.17744   4.243 3.35e-05 ***
## student.status1              -0.32085    0.16124  -1.990 0.047946 *  
## student.status2              -0.15677    0.16174  -0.969 0.333569    
## n_orientationSexual Minority  0.17819    0.14543   1.225 0.221878    
## n_ethnicity1                 -0.01581    0.14167  -0.112 0.911270    
## n_gender1                    -0.19234    0.49642  -0.387 0.698825    
## n_gender2                     0.31461    0.19067   1.650 0.100495    
## UGPG1                         0.10499    0.16314   0.644 0.520596    
## n_yearYear 2                 -0.35951    0.18275  -1.967 0.050529 .  
## n_yearYear 3                 -0.26070    0.17551  -1.485 0.138999    
## n_yearYear 4+                -0.47677    0.23182  -2.057 0.041008 *  
## age                          -0.02227    0.01611  -1.382 0.168346    
## SE_fi_z                       0.26218    0.06204   4.226 3.60e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8687 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3184, Adjusted R-squared:  0.2712 
## F-statistic: 6.741 on 14 and 202 DF,  p-value: 3.286e-11
summary(GAD_co2)
## 
## Call:
## lm(formula = GADz ~ SE_co_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9091 -0.6297 -0.1229  0.5540  2.3933 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.502630   0.390110   1.288  0.19913    
## SE_co_z                      -0.205024   0.065623  -3.124  0.00206 ** 
## n_disability1                 0.752314   0.185466   4.056 7.22e-05 ***
## student.status1              -0.151396   0.167339  -0.905  0.36673    
## student.status2              -0.023473   0.171181  -0.137  0.89107    
## n_orientationSexual Minority  0.184706   0.150317   1.229  0.22064    
## n_ethnicity1                  0.001541   0.147149   0.010  0.99165    
## n_gender1                    -0.356757   0.504336  -0.707  0.48018    
## n_gender2                     0.231231   0.195471   1.183  0.23828    
## UGPG1                         0.097105   0.166721   0.582  0.56095    
## n_yearYear 2                 -0.299460   0.185474  -1.615  0.10803    
## n_yearYear 3                 -0.266848   0.187147  -1.426  0.15551    
## n_yearYear 4+                -0.462886   0.235155  -1.968  0.05044 .  
## age                          -0.034508   0.016404  -2.104  0.03669 *  
## SE_fi_z                       0.253189   0.063535   3.985 9.54e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8777 on 194 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.3023, Adjusted R-squared:  0.252 
## F-statistic: 6.005 on 14 and 194 DF,  p-value: 8.627e-10
library(car)
## Loading required package: carData
## 
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
## 
##     recode
## The following object is masked from 'package:purrr':
## 
##     some
## The following object is masked from 'package:psych':
## 
##     logit
#### assumption checks GAD####
anova(GAD_aca1, GAD_aca2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SE_aca_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SE_aca_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    203 149.72                                
## 2    202 143.91  1    5.8142 8.1614 0.004727 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_acc1, GAD_acc2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SE_acc_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SE_acc_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    203 149.72                                
## 2    202 143.91  1    5.8142 8.1614 0.004727 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_co1, GAD_co2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SE_co_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SE_co_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    195 161.69                                  
## 2    194 149.45  1    12.234 15.881 9.543e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_fr1, GAD_fr2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SE_fr_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SE_fr_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
## 1    203 165.93                                 
## 2    202 152.46  1    13.479 17.86 3.599e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_PHQ1, GAD_PHQ2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ PHQz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ PHQz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F Pr(>F)
## 1    203 81.475                           
## 2    202 81.313  1   0.16204 0.4025 0.5265
anova(GAD_CUDIT1, GAD_CUDIT2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ CUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ CUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1     87 71.280                                  
## 2     86 54.775  1    16.505 25.914 2.077e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_AUDIT1, GAD_AUDIT2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ AUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ AUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 176.32                                  
## 2    202 162.24  1    14.074 17.523 4.236e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_unil1, GAD_unil2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ unil_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ unil_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 148.74                                  
## 2    202 138.87  1    9.8703 14.358 0.0001995 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_prel1, GAD_prel2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ prel_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ prel_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 161.47                                  
## 2    202 146.20  1    15.275 21.105 7.645e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_SA1, GAD_SA2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
## 1    203 129.88                                 
## 2    202 122.44  1    7.4376 12.27 0.0005665 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_SCI1, GAD_SCI2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    198 119.31                              
## 2    197 116.18  1    3.1315 5.3101 0.02224 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_PS1, GAD_PS2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    200 102.36                              
## 2    199 100.51  1    1.8563 3.6755 0.05665 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_wellbeing1, GAD_wellbeing2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ wellbeingz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ wellbeingz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F  Pr(>F)  
## 1    200 104.40                              
## 2    199 102.67  1    1.7329 3.3588 0.06834 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_SC1, GAD_SC2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 177.82                                  
## 2    202 164.08  1    13.744 16.921 5.671e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(GAD_perfectionism1, GAD_perfectionism2)
## Analysis of Variance Table
## 
## Model 1: GADz ~ perfectionismz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: GADz ~ perfectionismz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
## 1    198 146.09                                 
## 2    197 138.01  1    8.0771 11.53 0.0008284 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_aca2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.239   1  0.3358  0.562892    
## SE_aca_z        20.228   1 28.3944 2.631e-07 ***
## n_disability     7.182   1 10.0821  0.001732 ** 
## student.status   1.626   2  1.1409  0.321590    
## n_orientation    2.196   1  3.0821  0.080673 .  
## n_ethnicity      2.641   1  3.7078  0.055562 .  
## n_gender         2.411   2  1.6922  0.186716    
## UGPG             0.952   1  1.3357  0.249167    
## n_year           7.959   3  3.7239  0.012277 *  
## age              0.618   1  0.8673  0.352803    
## SE_fi_z          5.814   1  8.1614  0.004727 ** 
## Residuals      143.905 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_acc2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.239   1  0.3358  0.562892    
## SE_acc_z        20.228   1 28.3944 2.631e-07 ***
## n_disability     7.182   1 10.0821  0.001732 ** 
## student.status   1.626   2  1.1409  0.321590    
## n_orientation    2.196   1  3.0821  0.080673 .  
## n_ethnicity      2.641   1  3.7078  0.055562 .  
## n_gender         2.411   2  1.6922  0.186716    
## UGPG             0.952   1  1.3357  0.249167    
## n_year           7.959   3  3.7239  0.012277 *  
## age              0.618   1  0.8673  0.352803    
## SE_fi_z          5.814   1  8.1614  0.004727 ** 
## Residuals      143.905 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_co2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      1.279   1  1.6601  0.199130    
## SE_co_z          7.520   1  9.7611  0.002056 ** 
## n_disability    12.676   1 16.4540 7.218e-05 ***
## student.status   0.634   2  0.4115  0.663263    
## n_orientation    1.163   1  1.5099  0.220643    
## n_ethnicity      0.000   1  0.0001  0.991654    
## n_gender         2.105   2  1.3664  0.257467    
## UGPG             0.261   1  0.3392  0.560948    
## n_year           4.478   3  1.9374  0.124887    
## age              3.409   1  4.4255  0.036695 *  
## SE_fi_z         12.234   1 15.8806 9.543e-05 ***
## Residuals      149.452 194                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_fr2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.301   1  0.3984  0.528646    
## SE_fr_z         11.679   1 15.4741  0.000115 ***
## n_disability    13.590   1 18.0060 3.354e-05 ***
## student.status   3.178   2  2.1055  0.124454    
## n_orientation    1.133   1  1.5014  0.221878    
## n_ethnicity      0.009   1  0.0124  0.911270    
## n_gender         2.781   2  1.8426  0.161064    
## UGPG             0.313   1  0.4142  0.520596    
## n_year           5.341   3  2.3589  0.072776 .  
## age              1.443   1  1.9113  0.168346    
## SE_fi_z         13.479   1 17.8598 3.599e-05 ***
## Residuals      152.455 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_PHQ2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                Sum Sq  Df  F value    Pr(>F)    
## (Intercept)     0.017   1   0.0430  0.836018    
## PHQz           82.821   1 205.7467 < 2.2e-16 ***
## n_disability    0.488   1   1.2134  0.271973    
## student.status  0.215   2   0.2671  0.765874    
## n_orientation   0.719   1   1.7874  0.182752    
## n_ethnicity     0.285   1   0.7080  0.401095    
## n_gender        1.451   2   1.8025  0.167521    
## UGPG            0.146   1   0.3639  0.547040    
## n_year          4.959   3   4.1064  0.007426 ** 
## age             0.023   1   0.0575  0.810799    
## SE_fi_z         0.162   1   0.4025  0.526501    
## Residuals      81.313 202                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_CUDIT2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                Sum Sq Df F value    Pr(>F)    
## (Intercept)     0.263  1  0.4131   0.52209    
## CUDITz          1.678  1  2.6347   0.10821    
## n_disability    4.360  1  6.8457   0.01049 *  
## student.status  3.692  2  2.8983   0.06052 .  
## n_orientation   0.171  1  0.2685   0.60570    
## n_ethnicity     0.111  1  0.1743   0.67734    
## n_gender        4.333  2  3.4018   0.03786 *  
## UGPG            2.316  1  3.6355   0.05990 .  
## n_year          0.911  3  0.4766   0.69938    
## age             0.317  1  0.4977   0.48242    
## SE_fi_z        16.505  1 25.9143 2.077e-06 ***
## Residuals      54.775 86                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_AUDIT2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.470   1  0.5849   0.44527    
## AUDITz           1.890   1  2.3537   0.12655    
## n_disability    12.718   1 15.8340 9.637e-05 ***
## student.status   2.006   2  1.2489   0.28903    
## n_orientation    1.664   1  2.0720   0.15157    
## n_ethnicity      0.000   1  0.0000   0.99816    
## n_gender         2.046   2  1.2736   0.28207    
## UGPG             0.497   1  0.6193   0.43223    
## n_year           6.946   3  2.8828   0.03691 *  
## age              1.629   1  2.0284   0.15592    
## SE_fi_z         14.074   1 17.5227 4.236e-05 ***
## Residuals      162.243 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_unil2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.514   1  0.7473 0.3883541    
## unil_z          25.267   1 36.7535 6.481e-09 ***
## n_disability     8.185   1 11.9059 0.0006812 ***
## student.status   2.128   2  1.5479 0.2151974    
## n_orientation    0.982   1  1.4283 0.2334431    
## n_ethnicity      0.022   1  0.0321 0.8580024    
## n_gender         2.768   2  2.0130 0.1362569    
## UGPG             1.159   1  1.6856 0.1956586    
## n_year           5.179   3  2.5114 0.0597770 .  
## age              2.048   1  2.9789 0.0858848 .  
## SE_fi_z          9.870   1 14.3576 0.0001995 ***
## Residuals      138.867 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_prel2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.814   1  1.1245  0.290228    
## prel_z          17.934   1 24.7795 1.374e-06 ***
## n_disability     6.255   1  8.6430  0.003666 ** 
## student.status   1.622   2  1.1206  0.328091    
## n_orientation    0.147   1  0.2036  0.652324    
## n_ethnicity      0.261   1  0.3601  0.549112    
## n_gender         4.340   2  2.9981  0.052105 .  
## UGPG             0.919   1  1.2691  0.261267    
## n_year           3.668   3  1.6896  0.170451    
## age              2.452   1  3.3881  0.067135 .  
## SE_fi_z         15.275   1 21.1053 7.645e-06 ***
## Residuals      146.199 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_SA2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.019   1  0.0317 0.8587744    
## SAz             41.693   1 68.7834 1.534e-14 ***
## n_disability     7.183   1 11.8499 0.0007008 ***
## student.status   0.703   2  0.5799 0.5608798    
## n_orientation    0.036   1  0.0601 0.8066358    
## n_ethnicity      0.029   1  0.0475 0.8277317    
## n_gender         1.926   2  1.5891 0.2066421    
## UGPG             0.163   1  0.2686 0.6048533    
## n_year           3.897   3  2.1431 0.0959927 .  
## age              0.282   1  0.4652 0.4959756    
## SE_fi_z          7.438   1 12.2703 0.0005665 ***
## Residuals      122.441 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_SCI2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.040   1  0.0685  0.793746    
## SCInz           42.310   1 71.7460 5.622e-15 ***
## n_disability     4.842   1  8.2100  0.004618 ** 
## student.status   0.960   2  0.8138  0.444667    
## n_orientation    0.470   1  0.7974  0.372962    
## n_ethnicity      0.054   1  0.0915  0.762646    
## n_gender         3.155   2  2.6746  0.071440 .  
## UGPG             0.458   1  0.7759  0.379466    
## n_year           4.367   3  2.4682  0.063284 .  
## age              0.873   1  1.4807  0.225115    
## SE_fi_z          3.132   1  5.3101  0.022244 *  
## Residuals      116.176 197                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_PS2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df  F value  Pr(>F)    
## (Intercept)      0.183   1   0.3615 0.54835    
## PSz             62.288   1 123.3282 < 2e-16 ***
## n_disability     2.063   1   4.0840 0.04463 *  
## student.status   0.013   2   0.0128 0.98732    
## n_orientation    0.763   1   1.5098 0.22063    
## n_ethnicity      0.725   1   1.4349 0.23239    
## n_gender         2.990   2   2.9596 0.05412 .  
## UGPG             0.446   1   0.8839 0.34827    
## n_year           5.606   3   3.7000 0.01269 *  
## age              0.309   1   0.6112 0.43526    
## SE_fi_z          1.856   1   3.6755 0.05665 .  
## Residuals      100.507 199                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_wellbeing2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df  F value  Pr(>F)    
## (Intercept)      0.353   1   0.6845 0.40904    
## wellbeingz      60.124   1 116.5357 < 2e-16 ***
## n_disability     3.100   1   6.0080 0.01510 *  
## student.status   0.043   2   0.0413 0.95957    
## n_orientation    1.320   1   2.5590 0.11126    
## n_ethnicity      0.001   1   0.0017 0.96695    
## n_gender         2.705   2   2.6213 0.07522 .  
## UGPG             0.093   1   0.1809 0.67103    
## n_year           4.736   3   3.0600 0.02933 *  
## age              0.001   1   0.0015 0.96920    
## SE_fi_z          1.733   1   3.3588 0.06834 .  
## Residuals      102.670 199                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_SC2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.776   1  0.9558 0.3294276    
## SCz              0.057   1  0.0707 0.7905999    
## n_disability    12.486   1 15.3714 0.0001209 ***
## student.status   2.113   2  1.3005 0.2746643    
## n_orientation    1.845   1  2.2712 0.1333580    
## n_ethnicity      0.011   1  0.0132 0.9085105    
## n_gender         1.665   2  1.0247 0.3607453    
## UGPG             0.697   1  0.8585 0.3552768    
## n_year           6.347   3  2.6046 0.0529811 .  
## age              2.102   1  2.5874 0.1092806    
## SE_fi_z         13.744   1 16.9209 5.671e-05 ***
## Residuals      164.076 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(GAD_perfectionism2, type=3)
## Anova Table (Type III tests)
## 
## Response: GADz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.132   1  0.1887 0.6644936    
## perfectionismz  20.476   1 29.2280 1.849e-07 ***
## n_disability     7.906   1 11.2854 0.0009379 ***
## student.status   2.217   2  1.5826 0.2080469    
## n_orientation    0.808   1  1.1540 0.2840248    
## n_ethnicity      0.005   1  0.0065 0.9357341    
## n_gender         1.694   2  1.2087 0.3007806    
## UGPG             0.956   1  1.3649 0.2441039    
## n_year           5.235   3  2.4910 0.0614500 .  
## age              1.049   1  1.4978 0.2224738    
## SE_fi_z          8.077   1 11.5295 0.0008284 ***
## Residuals      138.010 197                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist(resid(GAD_PHQ2))

hist(resid(GAD_CUDIT2))

hist(resid(GAD_AUDIT2))

hist(resid(GAD_unil2))

hist(resid(GAD_prel2))

hist(resid(GAD_SA2))

hist(resid(GAD_SCI2))

hist(resid(GAD_PS2))

hist(resid(GAD_wellbeing2))

hist(resid(GAD_SC2))

hist(resid(GAD_perfectionism2))

hist(resid(GAD_aca2))

hist(resid(GAD_acc2))

hist(resid(GAD_fr2))

hist(resid(GAD_co2))

skewness(resid(GAD_PHQ2))
## [1] 0.450141
skewness(resid(GAD_CUDIT2))
## [1] 0.4764829
skewness(resid(GAD_AUDIT2))
## [1] 0.5936116
skewness(resid(GAD_unil2))
## [1] 0.6091435
skewness(resid(GAD_prel2))
## [1] 0.4996352
skewness(resid(GAD_SA2))
## [1] 0.5139783
skewness(resid(GAD_SCI2))
## [1] 0.5855343
skewness(resid(GAD_PS2))
## [1] 0.4215832
skewness(resid(GAD_wellbeing2))
## [1] 0.5153961
skewness(resid(GAD_SC2))
## [1] 0.5556626
skewness(resid(GAD_perfectionism2))
## [1] 0.634839
skewness(resid(GAD_aca2))
## [1] 0.4442322
skewness(resid(GAD_acc2))
## [1] 0.4442322
skewness(resid(GAD_fr2))
## [1] 0.5771022
skewness(resid(GAD_co2))
## [1] 0.5253917
qqPlot(GAD_PHQ2)

## [1] 157 230
qqPlot(GAD_AUDIT2)

## [1]  90 197
qqPlot(GAD_unil2)

## [1] 86 90
qqPlot(GAD_prel2)

## [1]  35 141
qqPlot(GAD_SA2)

## [1]  90 197
qqPlot(GAD_SCI2)

## [1] 111 197
qqPlot(GAD_PS2)

## [1] 147 197
qqPlot(GAD_wellbeing2)

## [1]  35 161
qqPlot(GAD_SC2)

## [1]  90 197
qqPlot(GAD_perfectionism2)

## [1]  90 197
qqPlot(GAD_aca2)

## [1] 111 197
qqPlot(GAD_acc2)

## [1] 111 197
qqPlot(GAD_fr2)

## [1]  86 197
qqPlot(GAD_co2)

## [1]  90 197
a1 <- vif(GAD_PHQ2)
b2 <- vif(GAD_CUDIT2)
c3 <- vif(GAD_AUDIT2)
d4 <- vif(GAD_unil2)
e5 <- vif(GAD_prel2)
f6 <- vif(GAD_SA2)
g7 <- vif(GAD_SCI2)
h8 <- vif(GAD_PS2)
i9 <- vif(GAD_wellbeing2)
j10 <- vif(GAD_SC2)
k11 <- vif(GAD_perfectionism2)
l12 <- vif(GAD_aca2)
m13 <- vif(GAD_acc2)
n14 <- vif(GAD_fr2)
o15 <- vif(GAD_co2)

a1
##                    GVIF Df GVIF^(1/(2*Df))
## PHQz           1.421101  1        1.192099
## n_disability   1.398833  1        1.182723
## student.status 1.571843  2        1.119702
## n_orientation  1.144886  1        1.069994
## n_ethnicity    1.366058  1        1.168785
## n_gender       1.243750  2        1.056047
## UGPG           1.773439  1        1.331705
## n_year         1.461917  3        1.065337
## age            1.845703  1        1.358566
## SE_fi_z        1.269862  1        1.126881
b2
##                    GVIF Df GVIF^(1/(2*Df))
## CUDITz         1.309466  1        1.144319
## n_disability   1.235014  1        1.111312
## student.status 1.473269  2        1.101718
## n_orientation  1.219652  1        1.104378
## n_ethnicity    1.394761  1        1.181000
## n_gender       1.405155  2        1.088757
## UGPG           2.246895  1        1.498965
## n_year         2.166743  3        1.137543
## age            1.825997  1        1.351295
## SE_fi_z        1.306770  1        1.143140
c3
##                    GVIF Df GVIF^(1/(2*Df))
## AUDITz         1.064927  1        1.031953
## n_disability   1.278374  1        1.130652
## student.status 1.551259  2        1.116018
## n_orientation  1.145813  1        1.070427
## n_ethnicity    1.369165  1        1.170113
## n_gender       1.251139  2        1.057612
## UGPG           1.786080  1        1.336443
## n_year         1.459714  3        1.065070
## age            1.820758  1        1.349355
## SE_fi_z        1.117466  1        1.057103
d4
##                    GVIF Df GVIF^(1/(2*Df))
## unil_z         1.098716  1        1.048197
## n_disability   1.298968  1        1.139723
## student.status 1.551578  2        1.116075
## n_orientation  1.147791  1        1.071350
## n_ethnicity    1.363038  1        1.167492
## n_gender       1.247271  2        1.056794
## UGPG           1.772715  1        1.331433
## n_year         1.453297  3        1.064288
## age            1.788667  1        1.337411
## SE_fi_z        1.133631  1        1.064721
e5
##                    GVIF Df GVIF^(1/(2*Df))
## prel_z         1.286030  1        1.134033
## n_disability   1.344463  1        1.159510
## student.status 1.551097  2        1.115989
## n_orientation  1.203095  1        1.096857
## n_ethnicity    1.376138  1        1.173089
## n_gender       1.285717  2        1.064845
## UGPG           1.770194  1        1.330487
## n_year         1.475486  3        1.066979
## age            1.789427  1        1.337695
## SE_fi_z        1.114844  1        1.055862
f6
##                    GVIF Df GVIF^(1/(2*Df))
## SAz            1.220849  1        1.104920
## n_disability   1.298849  1        1.139670
## student.status 1.559688  2        1.117531
## n_orientation  1.179619  1        1.086103
## n_ethnicity    1.363066  1        1.167504
## n_gender       1.238401  2        1.054910
## UGPG           1.777106  1        1.333081
## n_year         1.486672  3        1.068323
## age            1.826113  1        1.351338
## SE_fi_z        1.144716  1        1.069914
g7
##                    GVIF Df GVIF^(1/(2*Df))
## SCInz          1.249145  1        1.117651
## n_disability   1.351606  1        1.162586
## student.status 1.571898  2        1.119711
## n_orientation  1.152501  1        1.073546
## n_ethnicity    1.372011  1        1.171329
## n_gender       1.247180  2        1.056774
## UGPG           1.769814  1        1.330343
## n_year         1.455428  3        1.064548
## age            1.782763  1        1.335202
## SE_fi_z        1.214412  1        1.102004
h8
##                    GVIF Df GVIF^(1/(2*Df))
## PSz            1.348801  1        1.161379
## n_disability   1.367460  1        1.169384
## student.status 1.567793  2        1.118980
## n_orientation  1.145029  1        1.070060
## n_ethnicity    1.371249  1        1.171003
## n_gender       1.252204  2        1.057837
## UGPG           1.767469  1        1.329462
## n_year         1.475302  3        1.066957
## age            1.797619  1        1.340753
## SE_fi_z        1.214669  1        1.102120
i9
##                    GVIF Df GVIF^(1/(2*Df))
## wellbeingz     1.321962  1        1.149766
## n_disability   1.344368  1        1.159469
## student.status 1.589420  2        1.122819
## n_orientation  1.141055  1        1.068202
## n_ethnicity    1.355534  1        1.164274
## n_gender       1.240061  2        1.055263
## UGPG           1.774370  1        1.332055
## n_year         1.456568  3        1.064687
## age            1.838201  1        1.355803
## SE_fi_z        1.221941  1        1.105414
j10
##                    GVIF Df GVIF^(1/(2*Df))
## SCz            1.072830  1        1.035775
## n_disability   1.277648  1        1.130331
## student.status 1.552465  2        1.116235
## n_orientation  1.148564  1        1.071711
## n_ethnicity    1.364543  1        1.168137
## n_gender       1.238397  2        1.054909
## UGPG           1.771189  1        1.330860
## n_year         1.443574  3        1.063098
## age            1.799097  1        1.341304
## SE_fi_z        1.158847  1        1.076498
k11
##                    GVIF Df GVIF^(1/(2*Df))
## perfectionismz 1.134169  1        1.064974
## n_disability   1.332899  1        1.154513
## student.status 1.554018  2        1.116514
## n_orientation  1.150180  1        1.072465
## n_ethnicity    1.370313  1        1.170604
## n_gender       1.246518  2        1.056634
## UGPG           1.767297  1        1.329397
## n_year         1.452111  3        1.064143
## age            1.787824  1        1.337095
## SE_fi_z        1.159032  1        1.076583
l12
##                    GVIF Df GVIF^(1/(2*Df))
## SE_aca_z       1.394807  1        1.181019
## n_disability   1.319175  1        1.148554
## student.status 1.555608  2        1.116799
## n_orientation  1.141541  1        1.068429
## n_ethnicity    1.542722  1        1.242063
## n_gender       1.243054  2        1.055899
## UGPG           1.770416  1        1.330570
## n_year         1.458436  3        1.064914
## age            1.829124  1        1.352451
## SE_fi_z        1.209841  1        1.099928
m13
##                    GVIF Df GVIF^(1/(2*Df))
## SE_acc_z       1.394807  1        1.181019
## n_disability   1.319175  1        1.148554
## student.status 1.555608  2        1.116799
## n_orientation  1.141541  1        1.068429
## n_ethnicity    1.542722  1        1.242063
## n_gender       1.243054  2        1.055899
## UGPG           1.770416  1        1.330570
## n_year         1.458436  3        1.064914
## age            1.829124  1        1.352451
## SE_fi_z        1.209841  1        1.099928
n14
##                    GVIF Df GVIF^(1/(2*Df))
## SE_fr_z        1.081821  1        1.040106
## n_disability   1.280302  1        1.131504
## student.status 1.572642  2        1.119844
## n_orientation  1.150569  1        1.072646
## n_ethnicity    1.362886  1        1.167427
## n_gender       1.258821  2        1.059232
## UGPG           1.781047  1        1.334559
## n_year         1.460243  3        1.065134
## age            1.799517  1        1.341461
## SE_fi_z        1.116678  1        1.056730
o15
##                    GVIF Df GVIF^(1/(2*Df))
## SE_co_z        1.127726  1        1.061944
## n_disability   1.301065  1        1.140642
## student.status 1.654736  2        1.134181
## n_orientation  1.145706  1        1.070377
## n_ethnicity    1.387863  1        1.178076
## n_gender       1.261269  2        1.059746
## UGPG           1.745027  1        1.320995
## n_year         1.470889  3        1.066424
## age            1.784916  1        1.336008
## SE_fi_z        1.113887  1        1.055408
ta1 <- 1/a1
tb2 <- 1/b2
tc3 <- 1/c3
td4 <- 1/d4
te5 <- 1/e5
tf6 <- 1/f6
tg7 <- 1/g7
th8 <- 1/h8
ti9 <- 1/i9
tj10 <- 1/j10
tk11 <- 1/k11
tl12 <- 1/l12
tm13 <- 1/m13
tn14 <- 1/n14
to15 <- 1/o15

ta1
##                     GVIF        Df GVIF^(1/(2*Df))
## PHQz           0.7036798 1.0000000       0.8388563
## n_disability   0.7148816 1.0000000       0.8455067
## student.status 0.6361958 0.5000000       0.8930951
## n_orientation  0.8734493 1.0000000       0.9345851
## n_ethnicity    0.7320333 1.0000000       0.8555895
## n_gender       0.8040199 0.5000000       0.9469274
## UGPG           0.5638762 1.0000000       0.7509169
## n_year         0.6840332 0.3333333       0.9386698
## age            0.5417990 1.0000000       0.7360700
## SE_fi_z        0.7874874 1.0000000       0.8874049
tb2
##                     GVIF        Df GVIF^(1/(2*Df))
## CUDITz         0.7636699 1.0000000       0.8738821
## n_disability   0.8097072 1.0000000       0.8998373
## student.status 0.6787625 0.5000000       0.9076731
## n_orientation  0.8199062 1.0000000       0.9054867
## n_ethnicity    0.7169689 1.0000000       0.8467402
## n_gender       0.7116655 0.5000000       0.9184785
## UGPG           0.4450586 1.0000000       0.6671271
## n_year         0.4615221 0.3333333       0.8790875
## age            0.5476459 1.0000000       0.7400310
## SE_fi_z        0.7652458 1.0000000       0.8747833
tc3
##                     GVIF        Df GVIF^(1/(2*Df))
## AUDITz         0.9390314 1.0000000       0.9690363
## n_disability   0.7822439 1.0000000       0.8844455
## student.status 0.6446375 0.5000000       0.8960431
## n_orientation  0.8727424 1.0000000       0.9342068
## n_ethnicity    0.7303723 1.0000000       0.8546182
## n_gender       0.7992714 0.5000000       0.9455262
## UGPG           0.5598852 1.0000000       0.7482548
## n_year         0.6850658 0.3333333       0.9389059
## age            0.5492217 1.0000000       0.7410949
## SE_fi_z        0.8948814 1.0000000       0.9459817
td4
##                     GVIF        Df GVIF^(1/(2*Df))
## unil_z         0.9101531 1.0000000       0.9540194
## n_disability   0.7698416 1.0000000       0.8774062
## student.status 0.6445052 0.5000000       0.8959971
## n_orientation  0.8712386 1.0000000       0.9334016
## n_ethnicity    0.7336554 1.0000000       0.8565368
## n_gender       0.8017504 0.5000000       0.9462585
## UGPG           0.5641064 1.0000000       0.7510702
## n_year         0.6880906 0.3333333       0.9395955
## age            0.5590755 1.0000000       0.7477135
## SE_fi_z        0.8821212 1.0000000       0.9392131
te5
##                     GVIF        Df GVIF^(1/(2*Df))
## prel_z         0.7775865 1.0000000       0.8818087
## n_disability   0.7437916 1.0000000       0.8624336
## student.status 0.6447048 0.5000000       0.8960665
## n_orientation  0.8311896 1.0000000       0.9116960
## n_ethnicity    0.7266714 1.0000000       0.8524502
## n_gender       0.7777762 0.5000000       0.9391039
## UGPG           0.5649097 1.0000000       0.7516048
## n_year         0.6777426 0.3333333       0.9372256
## age            0.5588381 1.0000000       0.7475547
## SE_fi_z        0.8969866 1.0000000       0.9470938
tf6
##                     GVIF        Df GVIF^(1/(2*Df))
## SAz            0.8191022 1.0000000       0.9050427
## n_disability   0.7699127 1.0000000       0.8774467
## student.status 0.6411538 0.5000000       0.8948301
## n_orientation  0.8477313 1.0000000       0.9207232
## n_ethnicity    0.7336403 1.0000000       0.8565281
## n_gender       0.8074927 0.5000000       0.9479483
## UGPG           0.5627127 1.0000000       0.7501418
## n_year         0.6726435 0.3333333       0.9360467
## age            0.5476112 1.0000000       0.7400076
## SE_fi_z        0.8735791 1.0000000       0.9346545
tg7
##                     GVIF        Df GVIF^(1/(2*Df))
## SCInz          0.8005478 1.0000000       0.8947334
## n_disability   0.7398608 1.0000000       0.8601516
## student.status 0.6361737 0.5000000       0.8930873
## n_orientation  0.8676784 1.0000000       0.9314926
## n_ethnicity    0.7288574 1.0000000       0.8537314
## n_gender       0.8018090 0.5000000       0.9462758
## UGPG           0.5650313 1.0000000       0.7516856
## n_year         0.6870832 0.3333333       0.9393661
## age            0.5609270 1.0000000       0.7489506
## SE_fi_z        0.8234435 1.0000000       0.9074379
th8
##                     GVIF        Df GVIF^(1/(2*Df))
## PSz            0.7413990 1.0000000       0.8610453
## n_disability   0.7312831 1.0000000       0.8551509
## student.status 0.6378393 0.5000000       0.8936713
## n_orientation  0.8733403 1.0000000       0.9345268
## n_ethnicity    0.7292624 1.0000000       0.8539686
## n_gender       0.7985920 0.5000000       0.9453252
## UGPG           0.5657806 1.0000000       0.7521839
## n_year         0.6778274 0.3333333       0.9372451
## age            0.5562913 1.0000000       0.7458494
## SE_fi_z        0.8232698 1.0000000       0.9073422
ti9
##                     GVIF        Df GVIF^(1/(2*Df))
## wellbeingz     0.7564515 1.0000000       0.8697422
## n_disability   0.7438439 1.0000000       0.8624638
## student.status 0.6291604 0.5000000       0.8906157
## n_orientation  0.8763820 1.0000000       0.9361527
## n_ethnicity    0.7377168 1.0000000       0.8589044
## n_gender       0.8064122 0.5000000       0.9476310
## UGPG           0.5635802 1.0000000       0.7507198
## n_year         0.6865452 0.3333333       0.9392435
## age            0.5440102 1.0000000       0.7375705
## SE_fi_z        0.8183701 1.0000000       0.9046381
tj10
##                     GVIF        Df GVIF^(1/(2*Df))
## SCz            0.9321145 1.0000000       0.9654608
## n_disability   0.7826883 1.0000000       0.8846968
## student.status 0.6441369 0.5000000       0.8958691
## n_orientation  0.8706520 1.0000000       0.9330874
## n_ethnicity    0.7328460 1.0000000       0.8560642
## n_gender       0.8074953 0.5000000       0.9479490
## UGPG           0.5645925 1.0000000       0.7513937
## n_year         0.6927253 0.3333333       0.9406474
## age            0.5558345 1.0000000       0.7455431
## SE_fi_z        0.8629267 1.0000000       0.9289385
tk11
##                     GVIF        Df GVIF^(1/(2*Df))
## perfectionismz 0.8817026 1.0000000       0.9389902
## n_disability   0.7502442 1.0000000       0.8661664
## student.status 0.6434930 0.5000000       0.8956451
## n_orientation  0.8694290 1.0000000       0.9324318
## n_ethnicity    0.7297600 1.0000000       0.8542599
## n_gender       0.8022349 0.5000000       0.9464014
## UGPG           0.5658359 1.0000000       0.7522206
## n_year         0.6886526 0.3333333       0.9397234
## age            0.5593392 1.0000000       0.7478898
## SE_fi_z        0.8627893 1.0000000       0.9288645
tl12
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_aca_z       0.7169452 1.0000000       0.8467262
## n_disability   0.7580493 1.0000000       0.8706603
## student.status 0.6428357 0.5000000       0.8954163
## n_orientation  0.8760092 1.0000000       0.9359536
## n_ethnicity    0.6482051 1.0000000       0.8051118
## n_gender       0.8044701 0.5000000       0.9470599
## UGPG           0.5648389 1.0000000       0.7515577
## n_year         0.6856658 0.3333333       0.9390429
## age            0.5467099 1.0000000       0.7393984
## SE_fi_z        0.8265546 1.0000000       0.9091505
tm13
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_acc_z       0.7169452 1.0000000       0.8467262
## n_disability   0.7580493 1.0000000       0.8706603
## student.status 0.6428357 0.5000000       0.8954163
## n_orientation  0.8760092 1.0000000       0.9359536
## n_ethnicity    0.6482051 1.0000000       0.8051118
## n_gender       0.8044701 0.5000000       0.9470599
## UGPG           0.5648389 1.0000000       0.7515577
## n_year         0.6856658 0.3333333       0.9390429
## age            0.5467099 1.0000000       0.7393984
## SE_fi_z        0.8265546 1.0000000       0.9091505
tn14
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_fr_z        0.9243671 1.0000000       0.9614401
## n_disability   0.7810660 1.0000000       0.8837794
## student.status 0.6358725 0.5000000       0.8929816
## n_orientation  0.8691353 1.0000000       0.9322742
## n_ethnicity    0.7337372 1.0000000       0.8565846
## n_gender       0.7943939 0.5000000       0.9440804
## UGPG           0.5614674 1.0000000       0.7493113
## n_year         0.6848177 0.3333333       0.9388492
## age            0.5557046 1.0000000       0.7454560
## SE_fi_z        0.8955134 1.0000000       0.9463157
to15
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_co_z        0.8867405 1.0000000       0.9416690
## n_disability   0.7686011 1.0000000       0.8766990
## student.status 0.6043260 0.5000000       0.8816939
## n_orientation  0.8728243 1.0000000       0.9342507
## n_ethnicity    0.7205320 1.0000000       0.8488416
## n_gender       0.7928521 0.5000000       0.9436220
## UGPG           0.5730570 1.0000000       0.7570053
## n_year         0.6798611 0.3333333       0.9377132
## age            0.5602503 1.0000000       0.7484987
## SE_fi_z        0.8977575 1.0000000       0.9475007
#### summary PHQ####

summary(PHQ_GAD)
## 
## Call:
## lm(formula = PHQz ~ GADz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.02243 -0.38427 -0.04824  0.38779  2.02454 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.001051   0.040834  -0.026    0.979    
## GADz         0.779354   0.040898  19.056   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6352 on 240 degrees of freedom
## Multiple R-squared:  0.6021, Adjusted R-squared:  0.6004 
## F-statistic: 363.1 on 1 and 240 DF,  p-value: < 2.2e-16
summary(PHQ_CUDIT)
## 
## Call:
## lm(formula = PHQz ~ CUDITz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3462 -0.8087 -0.2088  0.5163  2.3538 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.03374    0.08952   0.377    0.707
## CUDITz       0.12502    0.09079   1.377    0.171
## 
## Residual standard error: 0.9516 on 111 degrees of freedom
##   (129 observations deleted due to missingness)
## Multiple R-squared:  0.0168, Adjusted R-squared:  0.00794 
## F-statistic: 1.896 on 1 and 111 DF,  p-value: 0.1713
summary(PHQ_AUDIT)
## 
## Call:
## lm(formula = PHQz ~ AUDITz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6428 -0.7982 -0.2617  0.7388  2.9018 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002032   0.064473  -0.032    0.975
## AUDITz       0.090667   0.064354   1.409    0.160
## 
## Residual standard error: 1.003 on 240 degrees of freedom
## Multiple R-squared:  0.008203,   Adjusted R-squared:  0.00407 
## F-statistic: 1.985 on 1 and 240 DF,  p-value: 0.1602
summary(PHQ_unil)
## 
## Call:
## lm(formula = PHQz ~ unil_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4435 -0.5981 -0.1386  0.5584  3.0614 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.00684    0.05632  -0.121    0.903    
## unil_z       0.50307    0.05732   8.777 3.21e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8762 on 240 degrees of freedom
## Multiple R-squared:  0.243,  Adjusted R-squared:  0.2398 
## F-statistic: 77.03 on 1 and 240 DF,  p-value: 3.212e-16
summary(PHQ_prel)
## 
## Call:
## lm(formula = PHQz ~ prel_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8909 -0.6714 -0.1650  0.4383  3.3415 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.004671   0.059143  -0.079    0.937    
## prel_z       0.412938   0.059905   6.893 4.78e-11 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.92 on 240 degrees of freedom
## Multiple R-squared:  0.1653, Adjusted R-squared:  0.1618 
## F-statistic: 47.52 on 1 and 240 DF,  p-value: 4.782e-11
summary(PHQ_SA)
## 
## Call:
## lm(formula = PHQz ~ SAz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.80229 -0.65723 -0.07847  0.47282  2.14277 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 0.004722   0.054792   0.086    0.931    
## SAz         0.525035   0.053849   9.750   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8523 on 240 degrees of freedom
## Multiple R-squared:  0.2837, Adjusted R-squared:  0.2807 
## F-statistic: 95.07 on 1 and 240 DF,  p-value: < 2.2e-16
summary(PHQ_SCI)
## 
## Call:
## lm(formula = PHQz ~ SCInz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5460 -0.4685 -0.1460  0.4130  2.6126 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.01071    0.04961  -0.216    0.829    
## SCInz        0.66044    0.04947  13.351   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7622 on 234 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.4324, Adjusted R-squared:   0.43 
## F-statistic: 178.2 on 1 and 234 DF,  p-value: < 2.2e-16
summary(PHQ_PS)
## 
## Call:
## lm(formula = PHQz ~ PSz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.54098 -0.49414  0.00246  0.48474  2.22358 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.004161   0.045335  -0.092    0.927    
## PSz          0.725728   0.045407  15.983   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6994 on 236 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.5198, Adjusted R-squared:  0.5177 
## F-statistic: 255.4 on 1 and 236 DF,  p-value: < 2.2e-16
summary(PHQ_wellbeing)
## 
## Call:
## lm(formula = PHQz ~ wellbeingz, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44316 -0.52265 -0.01138  0.41444  2.01056 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.0007112  0.0430922  -0.017    0.987    
## wellbeingz  -0.7582034  0.0432076 -17.548   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6648 on 236 degrees of freedom
##   (4 observations deleted due to missingness)
## Multiple R-squared:  0.5661, Adjusted R-squared:  0.5643 
## F-statistic: 307.9 on 1 and 236 DF,  p-value: < 2.2e-16
summary(PHQ_SC)
## 
## Call:
## lm(formula = PHQz ~ SCz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.4472 -0.7900 -0.2215  0.6653  2.9040 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.003617   0.062671  -0.058    0.954    
## SCz         -0.250840   0.062611  -4.006 8.23e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9749 on 240 degrees of freedom
## Multiple R-squared:  0.06269,    Adjusted R-squared:  0.05878 
## F-statistic: 16.05 on 1 and 240 DF,  p-value: 8.23e-05
summary(PHQ_perfectionism)
## 
## Call:
## lm(formula = PHQz ~ perfectionismz, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8380 -0.6670 -0.2267  0.6056  2.5733 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    -0.009308   0.060621  -0.154    0.878    
## perfectionismz  0.393347   0.060596   6.491 5.04e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9313 on 234 degrees of freedom
##   (6 observations deleted due to missingness)
## Multiple R-squared:  0.1526, Adjusted R-squared:  0.149 
## F-statistic: 42.14 on 1 and 234 DF,  p-value: 5.037e-10
summary(PHQ_aca)
## 
## Call:
## lm(formula = PHQz ~ SE_aca_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4005 -0.6813 -0.1409  0.5896  2.5179 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.002951   0.058566  -0.050     0.96    
## SE_aca_z    -0.429806   0.058925  -7.294 4.36e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9111 on 240 degrees of freedom
## Multiple R-squared:  0.1815, Adjusted R-squared:  0.178 
## F-statistic:  53.2 on 1 and 240 DF,  p-value: 4.358e-12
summary(PHQ_fi)
## 
## Call:
## lm(formula = PHQz ~ SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7436 -0.7552 -0.1986  0.7418  2.5715 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.001878   0.059786  -0.031    0.975    
## SE_fi_z      0.383905   0.059688   6.432 6.76e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.93 on 240 degrees of freedom
## Multiple R-squared:  0.147,  Adjusted R-squared:  0.1435 
## F-statistic: 41.37 on 1 and 240 DF,  p-value: 6.763e-10
summary(PHQ_acc)
## 
## Call:
## lm(formula = PHQz ~ SE_acc_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4005 -0.6813 -0.1409  0.5896  2.5179 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.002951   0.058566  -0.050     0.96    
## SE_acc_z    -0.429806   0.058925  -7.294 4.36e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9111 on 240 degrees of freedom
## Multiple R-squared:  0.1815, Adjusted R-squared:  0.178 
## F-statistic:  53.2 on 1 and 240 DF,  p-value: 4.358e-12
summary(PHQ_fr)
## 
## Call:
## lm(formula = PHQz ~ SE_fr_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9000 -0.6861 -0.2354  0.7131  2.9589 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.005148   0.061831  -0.083    0.934    
## SE_fr_z     -0.297140   0.061865  -4.803 2.75e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9618 on 240 degrees of freedom
## Multiple R-squared:  0.08769,    Adjusted R-squared:  0.08389 
## F-statistic: 23.07 on 1 and 240 DF,  p-value: 2.754e-06
summary(PHQ_co)
## 
## Call:
## lm(formula = PHQz ~ SE_co_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5119 -0.7651 -0.2360  0.6931  2.9349 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)   
## (Intercept) -0.002001   0.064528  -0.031  0.97528   
## SE_co_z     -0.189891   0.064828  -2.929  0.00374 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.987 on 232 degrees of freedom
##   (8 observations deleted due to missingness)
## Multiple R-squared:  0.03566,    Adjusted R-squared:  0.03151 
## F-statistic:  8.58 on 1 and 232 DF,  p-value: 0.003738
summary(PHQ_GAD1)
## 
## Call:
## lm(formula = PHQz ~ GADz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9811 -0.3491 -0.0330  0.3100  1.9374 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.341023   0.276711   1.232   0.2192    
## GADz                          0.734377   0.047162  15.571   <2e-16 ***
## n_disability1                 0.279851   0.133460   2.097   0.0372 *  
## student.status1              -0.105651   0.117328  -0.900   0.3689    
## student.status2              -0.102245   0.116562  -0.877   0.3814    
## n_orientationSexual Minority -0.009257   0.105322  -0.088   0.9300    
## n_ethnicity1                  0.104151   0.102824   1.013   0.3123    
## n_gender1                     0.497653   0.360031   1.382   0.1684    
## n_gender2                    -0.042807   0.136203  -0.314   0.7536    
## UGPG1                        -0.056575   0.116307  -0.486   0.6272    
## n_yearYear 2                  0.042731   0.133643   0.320   0.7495    
## n_yearYear 3                  0.277016   0.127621   2.171   0.0311 *  
## n_yearYear 4+                 0.174930   0.169125   1.034   0.3022    
## age                          -0.018811   0.011675  -1.611   0.1087    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6306 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.6346, Adjusted R-squared:  0.6112 
## F-statistic: 27.12 on 13 and 203 DF,  p-value: < 2.2e-16
summary(PHQ_CUDIT1)
## 
## Call:
## lm(formula = PHQz ~ CUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.32664 -0.51339 -0.05933  0.38251  2.41662 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                   0.230599   0.620174   0.372  0.71092   
## CUDITz                        0.041704   0.095465   0.437  0.66330   
## n_disability1                 0.753368   0.233766   3.223  0.00179 **
## student.status1              -0.569060   0.218942  -2.599  0.01098 * 
## student.status2              -0.094166   0.246510  -0.382  0.70339   
## n_orientationSexual Minority  0.343045   0.196247   1.748  0.08399 . 
## n_ethnicity1                 -0.097102   0.209259  -0.464  0.64379   
## n_gender1                     1.174792   0.942252   1.247  0.21582   
## n_gender2                     0.067178   0.272326   0.247  0.80574   
## UGPG1                        -0.038978   0.242646  -0.161  0.87275   
## n_yearYear 2                  0.013051   0.256444   0.051  0.95953   
## n_yearYear 3                 -0.004024   0.259879  -0.015  0.98768   
## n_yearYear 4+                -0.266509   0.324865  -0.820  0.41425   
## age                          -0.015349   0.025979  -0.591  0.55617   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8443 on 87 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.2979, Adjusted R-squared:  0.193 
## F-statistic: 2.839 on 13 and 87 DF,  p-value: 0.001935
summary(PHQ_AUDIT1)
## 
## Call:
## lm(formula = PHQz ~ AUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5629 -0.6411 -0.2536  0.4768  2.7698 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.57447    0.41159   1.396   0.1643    
## AUDITz                        0.08132    0.06524   1.246   0.2141    
## n_disability1                 0.85364    0.18946   4.506 1.12e-05 ***
## student.status1              -0.32689    0.17186  -1.902   0.0586 .  
## student.status2              -0.13409    0.17227  -0.778   0.4373    
## n_orientationSexual Minority  0.20452    0.15432   1.325   0.1866    
## n_ethnicity1                  0.09754    0.15209   0.641   0.5220    
## n_gender1                     0.20364    0.53078   0.384   0.7016    
## n_gender2                     0.06209    0.20175   0.308   0.7586    
## UGPG1                        -0.04952    0.17233  -0.287   0.7741    
## n_yearYear 2                 -0.22696    0.19598  -1.158   0.2482    
## n_yearYear 3                 -0.02806    0.18717  -0.150   0.8810    
## n_yearYear 4+                -0.36141    0.24502  -1.475   0.1418    
## age                          -0.03234    0.01730  -1.869   0.0631 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9306 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2043, Adjusted R-squared:  0.1533 
## F-statistic: 4.009 on 13 and 203 DF,  p-value: 7.618e-06
summary(PHQ_unil1)
## 
## Call:
## lm(formula = PHQz ~ unil_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.50555 -0.52124 -0.09433  0.44209  2.37189 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.54067    0.35312   1.531   0.1273    
## unil_z                        0.48522    0.05820   8.337 1.14e-14 ***
## n_disability1                 0.66084    0.16566   3.989 9.25e-05 ***
## student.status1              -0.31093    0.14888  -2.089   0.0380 *  
## student.status2              -0.22445    0.14927  -1.504   0.1342    
## n_orientationSexual Minority  0.11433    0.13394   0.854   0.3943    
## n_ethnicity1                  0.07560    0.13146   0.575   0.5659    
## n_gender1                     0.34513    0.46000   0.750   0.4540    
## n_gender2                     0.19071    0.17497   1.090   0.2770    
## UGPG1                         0.05570    0.14904   0.374   0.7090    
## n_yearYear 2                 -0.22935    0.16957  -1.353   0.1777    
## n_yearYear 3                  0.08876    0.16190   0.548   0.5841    
## n_yearYear 4+                -0.20183    0.21267  -0.949   0.3437    
## age                          -0.03526    0.01487  -2.372   0.0186 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8063 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4027, Adjusted R-squared:  0.3645 
## F-statistic: 10.53 on 13 and 203 DF,  p-value: < 2.2e-16
summary(PHQ_prel1)
## 
## Call:
## lm(formula = PHQz ~ prel_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9695 -0.5612 -0.1168  0.4355  3.1035 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.65344    0.38826   1.683 0.093910 .  
## prel_z                        0.33032    0.07023   4.703 4.72e-06 ***
## n_disability1                 0.65527    0.18519   3.538 0.000499 ***
## student.status1              -0.29653    0.16395  -1.809 0.071977 .  
## student.status2              -0.16756    0.16397  -1.022 0.308060    
## n_orientationSexual Minority  0.05774    0.15064   0.383 0.701886    
## n_ethnicity1                  0.01723    0.14534   0.119 0.905750    
## n_gender1                    -0.01916    0.50834  -0.038 0.969970    
## n_gender2                     0.15338    0.19301   0.795 0.427739    
## UGPG1                        -0.01150    0.16364  -0.070 0.944059    
## n_yearYear 2                 -0.15633    0.18696  -0.836 0.404052    
## n_yearYear 3                  0.08650    0.17879   0.484 0.629037    
## n_yearYear 4+                -0.21540    0.23484  -0.917 0.360116    
## age                          -0.03674    0.01636  -2.246 0.025787 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8871 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.277,  Adjusted R-squared:  0.2307 
## F-statistic: 5.982 on 13 and 203 DF,  p-value: 2.25e-09
summary(PHQ_SA1)
## 
## Call:
## lm(formula = PHQz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.74077 -0.58737 -0.05173  0.38523  2.17537 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.302332   0.355378   0.851    0.396    
## SAz                           0.484253   0.058120   8.332 1.18e-14 ***
## n_disability1                 0.659210   0.165712   3.978 9.66e-05 ***
## student.status1              -0.202570   0.149685  -1.353    0.177    
## student.status2              -0.137253   0.149013  -0.921    0.358    
## n_orientationSexual Minority -0.007719   0.136109  -0.057    0.955    
## n_ethnicity1                  0.075291   0.131482   0.573    0.568    
## n_gender1                     0.160804   0.459871   0.350    0.727    
## n_gender2                     0.064460   0.174070   0.370    0.712    
## UGPG1                        -0.079785   0.148832  -0.536    0.592    
## n_yearYear 2                 -0.243196   0.169624  -1.434    0.153    
## n_yearYear 3                  0.169607   0.162904   1.041    0.299    
## n_yearYear 4+                -0.066684   0.214627  -0.311    0.756    
## age                          -0.018799   0.014997  -1.254    0.211    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8064 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4025, Adjusted R-squared:  0.3642 
## F-statistic: 10.52 on 13 and 203 DF,  p-value: < 2.2e-16
summary(PHQ_SCI1)
## 
## Call:
## lm(formula = PHQz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.54589 -0.48158 -0.09802  0.41215  2.51670 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.27720    0.32442   0.854  0.39388    
## SCInz                         0.62585    0.05569  11.238  < 2e-16 ***
## n_disability1                 0.46658    0.15450   3.020  0.00286 ** 
## student.status1              -0.16790    0.13823  -1.215  0.22594    
## student.status2              -0.04186    0.13812  -0.303  0.76217    
## n_orientationSexual Minority  0.05895    0.12431   0.474  0.63588    
## n_ethnicity1                  0.04216    0.12124   0.348  0.72843    
## n_gender1                     0.44520    0.41935   1.062  0.28969    
## n_gender2                     0.27079    0.15994   1.693  0.09202 .  
## UGPG1                        -0.01118    0.13608  -0.082  0.93462    
## n_yearYear 2                 -0.10895    0.15711  -0.693  0.48886    
## n_yearYear 3                  0.09178    0.14789   0.621  0.53557    
## n_yearYear 4+                -0.17995    0.19401  -0.928  0.35477    
## age                          -0.02603    0.01360  -1.914  0.05709 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7344 on 198 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.5112, Adjusted R-squared:  0.4791 
## F-statistic: 15.93 on 13 and 198 DF,  p-value: < 2.2e-16
summary(PHQ_PS1)
## 
## Call:
## lm(formula = PHQz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.49262 -0.46328 -0.02719  0.42726  2.23786 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.43351    0.30093   1.441   0.1513    
## PSz                           0.69393    0.05242  13.237   <2e-16 ***
## n_disability1                 0.34193    0.14475   2.362   0.0191 *  
## student.status1              -0.03309    0.12908  -0.256   0.7980    
## student.status2              -0.07873    0.12707  -0.620   0.5362    
## n_orientationSexual Minority  0.06178    0.11406   0.542   0.5886    
## n_ethnicity1                 -0.05802    0.11267  -0.515   0.6071    
## n_gender1                    -0.24682    0.39222  -0.629   0.5299    
## n_gender2                     0.02600    0.14787   0.176   0.8606    
## UGPG1                         0.01436    0.12664   0.113   0.9098    
## n_yearYear 2                 -0.04970    0.14674  -0.339   0.7352    
## n_yearYear 3                 -0.06301    0.13743  -0.459   0.6471    
## n_yearYear 4+                -0.14259    0.18082  -0.789   0.4313    
## age                          -0.02076    0.01269  -1.636   0.1034    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6848 on 200 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5728, Adjusted R-squared:  0.545 
## F-statistic: 20.63 on 13 and 200 DF,  p-value: < 2.2e-16
summary(PHQ_wellbeing1)
## 
## Call:
## lm(formula = PHQz ~ wellbeingz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.19650 -0.45149 -0.00361  0.39474  1.82129 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.123090   0.287232  -0.429  0.66872    
## wellbeingz                   -0.721758   0.048192 -14.977  < 2e-16 ***
## n_disability1                 0.378479   0.134969   2.804  0.00554 ** 
## student.status1               0.055210   0.122243   0.452  0.65202    
## student.status2              -0.081563   0.119482  -0.683  0.49562    
## n_orientationSexual Minority  0.103154   0.106970   0.964  0.33605    
## n_ethnicity1                  0.091138   0.105415   0.865  0.38831    
## n_gender1                     0.281026   0.367322   0.765  0.44513    
## n_gender2                     0.208908   0.139519   1.497  0.13588    
## UGPG1                        -0.060411   0.119037  -0.508  0.61236    
## n_yearYear 2                 -0.168542   0.137412  -1.227  0.22144    
## n_yearYear 3                  0.037198   0.129244   0.288  0.77379    
## n_yearYear 4+                -0.053075   0.170560  -0.311  0.75599    
## age                          -0.006772   0.012039  -0.563  0.57437    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.644 on 200 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.6222, Adjusted R-squared:  0.5976 
## F-statistic: 25.34 on 13 and 200 DF,  p-value: < 2.2e-16
summary(PHQ_SC1)
## 
## Call:
## lm(formula = PHQz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6007 -0.6409 -0.2210  0.4899  2.6999 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.586282   0.396556   1.478 0.140842    
## SCz                          -0.225257   0.062009  -3.633 0.000355 ***
## n_disability1                 0.837904   0.184278   4.547 9.35e-06 ***
## student.status1              -0.335716   0.167123  -2.009 0.045883 *  
## student.status2              -0.191025   0.167704  -1.139 0.256020    
## n_orientationSexual Minority  0.161145   0.150546   1.070 0.285707    
## n_ethnicity1                  0.068075   0.147656   0.461 0.645263    
## n_gender1                     0.277666   0.516416   0.538 0.591387    
## n_gender2                     0.052385   0.195405   0.268 0.788907    
## UGPG1                        -0.031291   0.166934  -0.187 0.851497    
## n_yearYear 2                 -0.211191   0.190370  -1.109 0.268582    
## n_yearYear 3                  0.005416   0.181343   0.030 0.976203    
## n_yearYear 4+                -0.314910   0.238133  -1.322 0.187519    
## age                          -0.031396   0.016722  -1.878 0.061882 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9052 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2471, Adjusted R-squared:  0.1989 
## F-statistic: 5.126 on 13 and 203 DF,  p-value: 7.489e-08
summary(PHQ_perfectionism1)
## 
## Call:
## lm(formula = PHQz ~ perfectionismz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5940 -0.5659 -0.1811  0.4530  2.5044 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.408714   0.384884   1.062 0.289568    
## perfectionismz                0.352681   0.061596   5.726 3.78e-08 ***
## n_disability1                 0.661932   0.181779   3.641 0.000346 ***
## student.status1              -0.311502   0.163013  -1.911 0.057462 .  
## student.status2              -0.127646   0.163373  -0.781 0.435548    
## n_orientationSexual Minority  0.137162   0.146993   0.933 0.351892    
## n_ethnicity1                  0.074300   0.143666   0.517 0.605613    
## n_gender1                     0.509068   0.498984   1.020 0.308874    
## n_gender2                     0.123814   0.188597   0.657 0.512262    
## UGPG1                         0.009881   0.161442   0.061 0.951257    
## n_yearYear 2                 -0.217217   0.185808  -1.169 0.243793    
## n_yearYear 3                  0.061866   0.175400   0.353 0.724678    
## n_yearYear 4+                -0.255310   0.229825  -1.111 0.267963    
## age                          -0.027517   0.016153  -1.704 0.090031 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8706 on 198 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.3131, Adjusted R-squared:  0.268 
## F-statistic: 6.943 on 13 and 198 DF,  p-value: 5.324e-11
summary(PHQ_aca1)
## 
## Call:
## lm(formula = PHQz ~ SE_aca_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8848 -0.5862 -0.1121  0.4608  2.3910 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.41780    0.36891   1.133  0.25874    
## SE_aca_z                     -0.45850    0.06592  -6.956 4.72e-11 ***
## n_disability1                 0.61966    0.17402   3.561  0.00046 ***
## student.status1              -0.26138    0.15532  -1.683  0.09393 .  
## student.status2              -0.20739    0.15536  -1.335  0.18342    
## n_orientationSexual Minority  0.20504    0.13886   1.477  0.14134    
## n_ethnicity1                 -0.24264    0.14473  -1.677  0.09517 .  
## n_gender1                     0.31294    0.47887   0.654  0.51417    
## n_gender2                     0.16422    0.18208   0.902  0.36819    
## UGPG1                         0.05284    0.15527   0.340  0.73398    
## n_yearYear 2                 -0.25638    0.17665  -1.451  0.14823    
## n_yearYear 3                 -0.08103    0.16848  -0.481  0.63109    
## n_yearYear 4+                -0.32083    0.22073  -1.453  0.14764    
## age                          -0.02167    0.01560  -1.389  0.16632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8395 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3525, Adjusted R-squared:  0.311 
## F-statistic: 8.501 on 13 and 203 DF,  p-value: 1.111e-13
summary(PHQ_fi1)
## 
## Call:
## lm(formula = PHQz ~ SE_fi_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7816 -0.6345 -0.1404  0.5734  2.3035 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.61406    0.38308   1.603   0.1105    
## SE_fi_z                       0.33218    0.06243   5.321 2.72e-07 ***
## n_disability1                 0.78353    0.17855   4.388 1.84e-05 ***
## student.status1              -0.28213    0.16184  -1.743   0.0828 .  
## student.status2              -0.17171    0.16178  -1.061   0.2898    
## n_orientationSexual Minority  0.12165    0.14588   0.834   0.4053    
## n_ethnicity1                  0.09810    0.14271   0.687   0.4926    
## n_gender1                     0.36586    0.49978   0.732   0.4650    
## n_gender2                     0.16852    0.19045   0.885   0.3773    
## UGPG1                         0.11820    0.16378   0.722   0.4713    
## n_yearYear 2                 -0.20612    0.18405  -1.120   0.2641    
## n_yearYear 3                  0.10797    0.17661   0.611   0.5417    
## n_yearYear 4+                -0.16551    0.23254  -0.712   0.4775    
## age                          -0.04120    0.01618  -2.547   0.0116 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8751 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.2963, Adjusted R-squared:  0.2513 
## F-statistic: 6.576 on 13 and 203 DF,  p-value: 2.052e-10
summary(PHQ_acc1)
## 
## Call:
## lm(formula = PHQz ~ SE_acc_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8848 -0.5862 -0.1121  0.4608  2.3910 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.41780    0.36891   1.133  0.25874    
## SE_acc_z                     -0.45850    0.06592  -6.956 4.72e-11 ***
## n_disability1                 0.61966    0.17402   3.561  0.00046 ***
## student.status1              -0.26138    0.15532  -1.683  0.09393 .  
## student.status2              -0.20739    0.15536  -1.335  0.18342    
## n_orientationSexual Minority  0.20504    0.13886   1.477  0.14134    
## n_ethnicity1                 -0.24264    0.14473  -1.677  0.09517 .  
## n_gender1                     0.31294    0.47887   0.654  0.51417    
## n_gender2                     0.16422    0.18208   0.902  0.36819    
## UGPG1                         0.05284    0.15527   0.340  0.73398    
## n_yearYear 2                 -0.25638    0.17665  -1.451  0.14823    
## n_yearYear 3                 -0.08103    0.16848  -0.481  0.63109    
## n_yearYear 4+                -0.32083    0.22073  -1.453  0.14764    
## age                          -0.02167    0.01560  -1.389  0.16632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8395 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3525, Adjusted R-squared:  0.311 
## F-statistic: 8.501 on 13 and 203 DF,  p-value: 1.111e-13
summary(PHQ_fr1)
## 
## Call:
## lm(formula = PHQz ~ SE_fr_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8316 -0.5459 -0.1510  0.4541  2.6344 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.42199    0.37884   1.114   0.2666    
## SE_fr_z                      -0.36172    0.06043  -5.985 9.65e-09 ***
## n_disability1                 0.89356    0.17547   5.092 8.05e-07 ***
## student.status1              -0.41033    0.15954  -2.572   0.0108 *  
## student.status2              -0.26202    0.16030  -1.635   0.1037    
## n_orientationSexual Minority  0.13485    0.14313   0.942   0.3473    
## n_ethnicity1                  0.08484    0.14042   0.604   0.5464    
## n_gender1                     0.32621    0.49143   0.664   0.5076    
## n_gender2                     0.19109    0.18763   1.018   0.3097    
## UGPG1                        -0.10259    0.15929  -0.644   0.5203    
## n_yearYear 2                 -0.23836    0.18117  -1.316   0.1898    
## n_yearYear 3                  0.04622    0.17274   0.268   0.7893    
## n_yearYear 4+                -0.21006    0.22755  -0.923   0.3570    
## age                          -0.02798    0.01593  -1.757   0.0804 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8613 on 203 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3185, Adjusted R-squared:  0.2748 
## F-statistic: 7.296 on 13 and 203 DF,  p-value: 1.173e-11
summary(PHQ_co1)
## 
## Call:
## lm(formula = PHQz ~ SE_co_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9065 -0.6173 -0.1693  0.4830  2.8025 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.78193    0.40638   1.924   0.0558 .  
## SE_co_z                      -0.15186    0.06830  -2.223   0.0273 *  
## n_disability1                 0.88469    0.19311   4.581 8.24e-06 ***
## student.status1              -0.22837    0.17421  -1.311   0.1915    
## student.status2              -0.08168    0.17837  -0.458   0.6475    
## n_orientationSexual Minority  0.16009    0.15592   1.027   0.3058    
## n_ethnicity1                  0.10773    0.15337   0.702   0.4833    
## n_gender1                     0.13119    0.52499   0.250   0.8029    
## n_gender2                     0.03399    0.20184   0.168   0.8664    
## UGPG1                        -0.06955    0.17101  -0.407   0.6847    
## n_yearYear 2                 -0.16955    0.19335  -0.877   0.3816    
## n_yearYear 3                  0.04867    0.19384   0.251   0.8020    
## n_yearYear 4+                -0.24012    0.24282  -0.989   0.3240    
## age                          -0.04315    0.01707  -2.527   0.0123 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.915 on 195 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.2295, Adjusted R-squared:  0.1782 
## F-statistic: 4.468 on 13 and 195 DF,  p-value: 1.225e-06
summary(PHQ_GAD2)
## 
## Call:
## lm(formula = PHQz ~ GADz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.71567 -0.36453 -0.05634  0.30811  2.04225 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.344254   0.270951   1.271  0.20535    
## GADz                          0.691344   0.048198  14.344  < 2e-16 ***
## n_disability1                 0.285016   0.130692   2.181  0.03035 *  
## student.status1              -0.097417   0.114915  -0.848  0.39759    
## student.status2              -0.115527   0.114214  -1.011  0.31299    
## n_orientationSexual Minority -0.037657   0.103531  -0.364  0.71644    
## n_ethnicity1                  0.108853   0.100695   1.081  0.28097    
## n_gender1                     0.544893   0.352860   1.544  0.12410    
## n_gender2                     0.018140   0.134791   0.135  0.89308    
## UGPG1                         0.008977   0.115809   0.078  0.93829    
## n_yearYear 2                  0.031177   0.130913   0.238  0.81201    
## n_yearYear 3                  0.309962   0.125409   2.472  0.01428 *  
## n_yearYear 4+                 0.221938   0.166288   1.335  0.18349    
## age                          -0.022385   0.011489  -1.948  0.05275 .  
## SE_fi_z                       0.143373   0.045975   3.118  0.00208 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6175 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.6514, Adjusted R-squared:  0.6272 
## F-statistic: 26.96 on 14 and 202 DF,  p-value: < 2.2e-16
summary(PHQ_CUDIT2)
## 
## Call:
## lm(formula = PHQz ~ CUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.53446 -0.50263 -0.07184  0.39489  2.12722 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.19501    0.56608   0.344  0.73132    
## CUDITz                        0.09189    0.08791   1.045  0.29879    
## n_disability1                 0.69218    0.21383   3.237  0.00172 ** 
## student.status1              -0.49852    0.20050  -2.486  0.01484 *  
## student.status2              -0.05343    0.22518  -0.237  0.81302    
## n_orientationSexual Minority  0.20396    0.18201   1.121  0.26558    
## n_ethnicity1                 -0.02980    0.19163  -0.155  0.87680    
## n_gender1                     1.54730    0.86433   1.790  0.07695 .  
## n_gender2                     0.22711    0.25132   0.904  0.36870    
## UGPG1                         0.29878    0.23501   1.271  0.20702    
## n_yearYear 2                  0.10301    0.23499   0.438  0.66222    
## n_yearYear 3                  0.16712    0.24051   0.695  0.48901    
## n_yearYear 4+                 0.22766    0.31804   0.716  0.47605    
## age                          -0.03115    0.02399  -1.298  0.19764    
## SE_fi_z                       0.35364    0.08234   4.295 4.58e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7706 on 86 degrees of freedom
##   (141 observations deleted due to missingness)
## Multiple R-squared:  0.4219, Adjusted R-squared:  0.3278 
## F-statistic: 4.483 on 14 and 86 DF,  p-value: 5.617e-06
summary(PHQ_AUDIT2)
## 
## Call:
## lm(formula = PHQz ~ AUDITz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7126 -0.6512 -0.1392  0.5919  2.3658 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.55586    0.38697   1.436   0.1524    
## AUDITz                        0.06450    0.06142   1.050   0.2949    
## n_disability1                 0.78803    0.17856   4.413 1.66e-05 ***
## student.status1              -0.27864    0.16183  -1.722   0.0866 .  
## student.status2              -0.16120    0.16204  -0.995   0.3210    
## n_orientationSexual Minority  0.11168    0.14615   0.764   0.4457    
## n_ethnicity1                  0.10827    0.14300   0.757   0.4499    
## n_gender1                     0.35255    0.49981   0.705   0.4814    
## n_gender2                     0.18603    0.19113   0.973   0.3316    
## UGPG1                         0.10126    0.16453   0.615   0.5390    
## n_yearYear 2                 -0.21629    0.18426  -1.174   0.2418    
## n_yearYear 3                  0.09001    0.17739   0.507   0.6124    
## n_yearYear 4+                -0.18036    0.23291  -0.774   0.4396    
## age                          -0.03892    0.01632  -2.385   0.0180 *  
## SE_fi_z                       0.32877    0.06250   5.260 3.65e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8749 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3001, Adjusted R-squared:  0.2516 
## F-statistic: 6.188 on 14 and 202 DF,  p-value: 3.383e-10
summary(PHQ_unil2)
## 
## Call:
## lm(formula = PHQz ~ unil_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.61977 -0.51099 -0.08458  0.39962  2.07146 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.52040    0.33394   1.558 0.120709    
## unil_z                        0.44909    0.05550   8.091 5.43e-14 ***
## n_disability1                 0.62091    0.15685   3.959 0.000104 ***
## student.status1              -0.27135    0.14100  -1.924 0.055698 .  
## student.status2              -0.23908    0.14118  -1.693 0.091915 .  
## n_orientationSexual Minority  0.04218    0.12747   0.331 0.741039    
## n_ethnicity1                  0.08746    0.12433   0.703 0.482571    
## n_gender1                     0.45743    0.43555   1.050 0.294865    
## n_gender2                     0.28693    0.16657   1.723 0.086494 .  
## UGPG1                         0.17181    0.14283   1.203 0.230428    
## n_yearYear 2                 -0.22152    0.16035  -1.381 0.168677    
## n_yearYear 3                  0.17655    0.15410   1.146 0.253279    
## n_yearYear 4+                -0.06419    0.20298  -0.316 0.752141    
## age                          -0.04026    0.01409  -2.857 0.004724 ** 
## SE_fi_z                       0.27443    0.05486   5.003 1.23e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7624 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4686, Adjusted R-squared:  0.4317 
## F-statistic: 12.72 on 14 and 202 DF,  p-value: < 2.2e-16
summary(PHQ_prel2)
## 
## Call:
## lm(formula = PHQz ~ prel_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9226 -0.5607 -0.0937  0.5448  2.7125 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.61872    0.36090   1.714 0.087989 .  
## prel_z                        0.33748    0.06528   5.169 5.63e-07 ***
## n_disability1                 0.58466    0.17256   3.388 0.000846 ***
## student.status1              -0.24509    0.15263  -1.606 0.109890    
## student.status2              -0.19313    0.15246  -1.267 0.206703    
## n_orientationSexual Minority -0.04412    0.14113  -0.313 0.754875    
## n_ethnicity1                  0.02957    0.13509   0.219 0.826982    
## n_gender1                     0.12562    0.47313   0.266 0.790895    
## n_gender2                     0.28833    0.18092   1.594 0.112558    
## UGPG1                         0.13978    0.15435   0.906 0.366233    
## n_yearYear 2                 -0.14681    0.17377  -0.845 0.399180    
## n_yearYear 3                  0.20541    0.16745   1.227 0.221367    
## n_yearYear 4+                -0.02996    0.22064  -0.136 0.892122    
## age                          -0.04295    0.01524  -2.818 0.005320 ** 
## SE_fi_z                       0.33798    0.05883   5.745 3.34e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8245 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3785, Adjusted R-squared:  0.3355 
## F-statistic: 8.788 on 14 and 202 DF,  p-value: 8.019e-15
summary(PHQ_SA2)
## 
## Call:
## lm(formula = PHQz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5982 -0.5251 -0.0686  0.4351  2.1079 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.30608    0.33820   0.905 0.366532    
## SAz                           0.44135    0.05606   7.873 2.08e-13 ***
## n_disability1                 0.62469    0.15787   3.957 0.000105 ***
## student.status1              -0.17488    0.14257  -1.227 0.221382    
## student.status2              -0.15753    0.14187  -1.110 0.268161    
## n_orientationSexual Minority -0.06371    0.13007  -0.490 0.624821    
## n_ethnicity1                  0.08677    0.12515   0.693 0.488903    
## n_gender1                     0.28175    0.43839   0.643 0.521151    
## n_gender2                     0.16449    0.16701   0.985 0.325847    
## UGPG1                         0.04119    0.14395   0.286 0.775080    
## n_yearYear 2                 -0.23425    0.16143  -1.451 0.148319    
## n_yearYear 3                  0.24428    0.15584   1.568 0.118562    
## n_yearYear 4+                 0.04977    0.20574   0.242 0.809112    
## age                          -0.02501    0.01433  -1.745 0.082465 .  
## SE_fi_z                       0.26114    0.05549   4.706 4.67e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7674 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4616, Adjusted R-squared:  0.4242 
## F-statistic: 12.37 on 14 and 202 DF,  p-value: < 2.2e-16
summary(PHQ_SCI2)
## 
## Call:
## lm(formula = PHQz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.44748 -0.45327 -0.09685  0.34318  2.30842 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.28251    0.31546   0.896 0.371583    
## SCInz                         0.57192    0.05627  10.163  < 2e-16 ***
## n_disability1                 0.46536    0.15023   3.098 0.002236 ** 
## student.status1              -0.14802    0.13453  -1.100 0.272540    
## student.status2              -0.07819    0.13470  -0.580 0.562262    
## n_orientationSexual Minority  0.01221    0.12160   0.100 0.920115    
## n_ethnicity1                  0.05649    0.11797   0.479 0.632570    
## n_gender1                     0.50695    0.40815   1.242 0.215684    
## n_gender2                     0.32489    0.15628   2.079 0.038926 *  
## UGPG1                         0.07590    0.13461   0.564 0.573508    
## n_yearYear 2                 -0.11051    0.15278  -0.723 0.470310    
## n_yearYear 3                  0.15227    0.14482   1.051 0.294372    
## n_yearYear 4+                -0.08929    0.19040  -0.469 0.639609    
## age                          -0.03006    0.01328  -2.264 0.024649 *  
## SE_fi_z                       0.18938    0.05377   3.522 0.000533 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7141 on 197 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.5401, Adjusted R-squared:  0.5075 
## F-statistic: 16.53 on 14 and 197 DF,  p-value: < 2.2e-16
summary(PHQ_PS2)
## 
## Call:
## lm(formula = PHQz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.34992 -0.40792  0.01846  0.40110  2.09464 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.431167   0.293958   1.467  0.14402    
## PSz                           0.645065   0.053363  12.088  < 2e-16 ***
## n_disability1                 0.345003   0.141398   2.440  0.01557 *  
## student.status1              -0.027692   0.126103  -0.220  0.82641    
## student.status2              -0.099548   0.124292  -0.801  0.42413    
## n_orientationSexual Minority  0.025525   0.111970   0.228  0.81991    
## n_ethnicity1                 -0.044041   0.110138  -0.400  0.68968    
## n_gender1                    -0.144045   0.384432  -0.375  0.70829    
## n_gender2                     0.090838   0.145809   0.623  0.53400    
## UGPG1                         0.085932   0.125647   0.684  0.49483    
## n_yearYear 2                 -0.054724   0.143351  -0.382  0.70306    
## n_yearYear 3                 -0.001642   0.135560  -0.012  0.99035    
## n_yearYear 4+                -0.068689   0.178084  -0.386  0.70012    
## age                          -0.024717   0.012456  -1.984  0.04859 *  
## SE_fi_z                       0.163321   0.050161   3.256  0.00133 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6689 on 199 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.5944, Adjusted R-squared:  0.5658 
## F-statistic: 20.83 on 14 and 199 DF,  p-value: < 2.2e-16
summary(PHQ_wellbeing2)
## 
## Call:
## lm(formula = PHQz ~ wellbeingz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.38607 -0.43672 -0.02793  0.44342  1.70940 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  -0.091895   0.281685  -0.326  0.74459    
## wellbeingz                   -0.678139   0.049364 -13.737  < 2e-16 ***
## n_disability1                 0.378096   0.132275   2.858  0.00471 ** 
## student.status1               0.055125   0.119803   0.460  0.64592    
## student.status2              -0.099669   0.117248  -0.850  0.39631    
## n_orientationSexual Minority  0.068373   0.105458   0.648  0.51751    
## n_ethnicity1                  0.094206   0.103316   0.912  0.36296    
## n_gender1                     0.339047   0.360495   0.941  0.34810    
## n_gender2                     0.255087   0.137576   1.854  0.06520 .  
## UGPG1                         0.007400   0.118776   0.062  0.95039    
## n_yearYear 2                 -0.165464   0.134673  -1.229  0.22066    
## n_yearYear 3                  0.085253   0.127648   0.668  0.50499    
## n_yearYear 4+                 0.007135   0.168326   0.042  0.96623    
## age                          -0.011085   0.011883  -0.933  0.35206    
## SE_fi_z                       0.144215   0.047467   3.038  0.00270 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6311 on 199 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.6389, Adjusted R-squared:  0.6135 
## F-statistic: 25.15 on 14 and 199 DF,  p-value: < 2.2e-16
summary(PHQ_SC2)
## 
## Call:
## lm(formula = PHQz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6484 -0.6483 -0.1455  0.5402  2.2208 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.57101    0.37701   1.515  0.13144    
## SCz                          -0.16930    0.06011  -2.816  0.00534 ** 
## n_disability1                 0.78212    0.17558   4.454 1.39e-05 ***
## student.status1              -0.29015    0.15917  -1.823  0.06980 .  
## student.status2              -0.20222    0.15945  -1.268  0.20617    
## n_orientationSexual Minority  0.08870    0.14393   0.616  0.53840    
## n_ethnicity1                  0.08431    0.14042   0.600  0.54887    
## n_gender1                     0.39501    0.49157   0.804  0.42259    
## n_gender2                     0.16556    0.18729   0.884  0.37776    
## UGPG1                         0.10188    0.16115   0.632  0.52796    
## n_yearYear 2                 -0.20467    0.18099  -1.131  0.25945    
## n_yearYear 3                  0.10530    0.17367   0.606  0.54500    
## n_yearYear 4+                -0.16153    0.22867  -0.706  0.48076    
## age                          -0.03776    0.01595  -2.367  0.01889 *  
## SE_fi_z                       0.29767    0.06260   4.755 3.77e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8606 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3229, Adjusted R-squared:  0.276 
## F-statistic: 6.881 on 14 and 202 DF,  p-value: 1.831e-11
summary(PHQ_perfectionism2)
## 
## Call:
## lm(formula = PHQz ~ perfectionismz + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8874 -0.5798 -0.1561  0.4790  2.1946 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.40126    0.36617   1.096 0.274487    
## perfectionismz                0.30477    0.05949   5.123 7.15e-07 ***
## n_disability1                 0.63617    0.17303   3.677 0.000305 ***
## student.status1              -0.26321    0.15543  -1.693 0.091956 .  
## student.status2              -0.17123    0.15571  -1.100 0.272823    
## n_orientationSexual Minority  0.05722    0.14089   0.406 0.685101    
## n_ethnicity1                  0.09183    0.13673   0.672 0.502615    
## n_gender1                     0.59166    0.47505   1.245 0.214438    
## n_gender2                     0.22345    0.18069   1.237 0.217697    
## UGPG1                         0.13758    0.15601   0.882 0.378915    
## n_yearYear 2                 -0.20567    0.17679  -1.163 0.246090    
## n_yearYear 3                  0.15607    0.16809   0.928 0.354288    
## n_yearYear 4+                -0.11004    0.22085  -0.498 0.618869    
## age                          -0.03342    0.01542  -2.168 0.031392 *  
## SE_fi_z                       0.28420    0.06093   4.665 5.70e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8283 on 197 degrees of freedom
##   (30 observations deleted due to missingness)
## Multiple R-squared:  0.3814, Adjusted R-squared:  0.3375 
## F-statistic: 8.677 on 14 and 197 DF,  p-value: 1.506e-14
summary(PHQ_aca2)
## 
## Call:
## lm(formula = PHQz ~ SE_aca_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6666 -0.5387 -0.1146  0.4709  2.2167 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.43003    0.35684   1.205 0.229567    
## SE_aca_z                     -0.38628    0.06643  -5.815 2.34e-08 ***
## n_disability1                 0.60984    0.16834   3.623 0.000369 ***
## student.status1              -0.23747    0.15035  -1.579 0.115812    
## student.status2              -0.21525    0.15029  -1.432 0.153616    
## n_orientationSexual Minority  0.13912    0.13539   1.028 0.305373    
## n_ethnicity1                 -0.18157    0.14087  -1.289 0.198897    
## n_gender1                     0.40171    0.46375   0.866 0.387400    
## n_gender2                     0.23596    0.17709   1.332 0.184223    
## UGPG1                         0.14407    0.15202   0.948 0.344414    
## n_yearYear 2                 -0.24403    0.17089  -1.428 0.154837    
## n_yearYear 3                  0.01145    0.16470   0.069 0.944666    
## n_yearYear 4+                -0.19839    0.21583  -0.919 0.359103    
## age                          -0.02806    0.01518  -1.848 0.065998 .  
## SE_fi_z                       0.23363    0.06035   3.871 0.000146 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.812 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3972, Adjusted R-squared:  0.3554 
## F-statistic: 9.508 on 14 and 202 DF,  p-value: 4.869e-16
summary(PHQ_acc2)
## 
## Call:
## lm(formula = PHQz ~ SE_acc_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.6666 -0.5387 -0.1146  0.4709  2.2167 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.43003    0.35684   1.205 0.229567    
## SE_acc_z                     -0.38628    0.06643  -5.815 2.34e-08 ***
## n_disability1                 0.60984    0.16834   3.623 0.000369 ***
## student.status1              -0.23747    0.15035  -1.579 0.115812    
## student.status2              -0.21525    0.15029  -1.432 0.153616    
## n_orientationSexual Minority  0.13912    0.13539   1.028 0.305373    
## n_ethnicity1                 -0.18157    0.14087  -1.289 0.198897    
## n_gender1                     0.40171    0.46375   0.866 0.387400    
## n_gender2                     0.23596    0.17709   1.332 0.184223    
## UGPG1                         0.14407    0.15202   0.948 0.344414    
## n_yearYear 2                 -0.24403    0.17089  -1.428 0.154837    
## n_yearYear 3                  0.01145    0.16470   0.069 0.944666    
## n_yearYear 4+                -0.19839    0.21583  -0.919 0.359103    
## age                          -0.02806    0.01518  -1.848 0.065998 .  
## SE_fi_z                       0.23363    0.06035   3.871 0.000146 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.812 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.3972, Adjusted R-squared:  0.3554 
## F-statistic: 9.508 on 14 and 202 DF,  p-value: 4.869e-16
summary(PHQ_fr2)
## 
## Call:
## lm(formula = PHQz ~ SE_fr_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.9692 -0.5525 -0.1070  0.4926  2.3713 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.39811    0.35420   1.124   0.2624    
## SE_fr_z                      -0.34778    0.05655  -6.149 4.09e-09 ***
## n_disability1                 0.82967    0.16445   5.045 1.01e-06 ***
## student.status1              -0.35989    0.14943  -2.408   0.0169 *  
## student.status2              -0.28110    0.14990  -1.875   0.0622 .  
## n_orientationSexual Minority  0.04597    0.13478   0.341   0.7334    
## n_ethnicity1                  0.09774    0.13130   0.744   0.4575    
## n_gender1                     0.46238    0.46009   1.005   0.3161    
## n_gender2                     0.30919    0.17671   1.750   0.0817 .  
## UGPG1                         0.04141    0.15120   0.274   0.7844    
## n_yearYear 2                 -0.22970    0.16938  -1.356   0.1766    
## n_yearYear 3                  0.15358    0.16266   0.944   0.3462    
## n_yearYear 4+                -0.04431    0.21485  -0.206   0.8368    
## age                          -0.03403    0.01493  -2.280   0.0237 *  
## SE_fi_z                       0.31635    0.05750   5.502 1.13e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8052 on 202 degrees of freedom
##   (25 observations deleted due to missingness)
## Multiple R-squared:  0.4073, Adjusted R-squared:  0.3662 
## F-statistic: 9.915 on 14 and 202 DF,  p-value: < 2.2e-16
summary(PHQ_co2)
## 
## Call:
## lm(formula = PHQz ~ SE_co_z + n_disability + student.status + 
##     n_orientation + n_ethnicity + n_gender + UGPG + n_year + 
##     age + SE_fi_z, data = pass)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8658 -0.6230 -0.1332  0.5583  2.4207 
## 
## Coefficients:
##                              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   0.70740    0.38303   1.847   0.0663 .  
## SE_co_z                      -0.13333    0.06443  -2.069   0.0398 *  
## n_disability1                 0.83965    0.18210   4.611 7.26e-06 ***
## student.status1              -0.18523    0.16430  -1.127   0.2610    
## student.status2              -0.10770    0.16807  -0.641   0.5224    
## n_orientationSexual Minority  0.08520    0.14759   0.577   0.5644    
## n_ethnicity1                  0.12156    0.14448   0.841   0.4012    
## n_gender1                     0.26703    0.49518   0.539   0.5903    
## n_gender2                     0.16794    0.19192   0.875   0.3826    
## UGPG1                         0.07893    0.16369   0.482   0.6302    
## n_yearYear 2                 -0.17017    0.18211  -0.934   0.3512    
## n_yearYear 3                  0.15441    0.18375   0.840   0.4018    
## n_yearYear 4+                -0.07911    0.23088  -0.343   0.7322    
## age                          -0.04762    0.01611  -2.957   0.0035 ** 
## SE_fi_z                       0.31704    0.06238   5.082 8.74e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8618 on 194 degrees of freedom
##   (33 observations deleted due to missingness)
## Multiple R-squared:  0.3201, Adjusted R-squared:  0.271 
## F-statistic: 6.523 on 14 and 194 DF,  p-value: 9.903e-11
anova(PHQ_aca1, PHQ_aca2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SE_aca_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SE_aca_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 143.06                                  
## 2    202 133.18  1    9.8798 14.986 0.0001462 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_acc1, PHQ_acc2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SE_acc_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SE_acc_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 143.06                                  
## 2    202 133.18  1    9.8798 14.986 0.0001462 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_co1, PHQ_co2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SE_co_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SE_co_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq     F   Pr(>F)    
## 1    195 163.25                                
## 2    194 144.07  1    19.182 25.83 8.74e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_fr1, PHQ_fr2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SE_fr_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SE_fr_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 150.58                                  
## 2    202 130.95  1    19.625 30.272 1.129e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_GAD1, PHQ_GAD2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ GADz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ GADz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    203 80.728                                
## 2    202 77.020  1     3.708 9.7249 0.002083 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_CUDIT1, PHQ_CUDIT2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ CUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ CUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1     87 62.021                                  
## 2     86 51.068  1    10.953 18.445 4.578e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_AUDIT1, PHQ_AUDIT2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ AUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ AUDITz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 175.81                                  
## 2    202 154.62  1    21.181 27.671 3.654e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_unil1, PHQ_unil2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ unil_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ unil_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 131.96                                  
## 2    202 117.42  1    14.548 25.028 1.225e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_prel1, PHQ_prel2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ prel_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ prel_z + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq     F    Pr(>F)    
## 1    203 159.74                                 
## 2    202 137.31  1    22.438 33.01 3.339e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_SA1, PHQ_SA2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SAz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    203 132.01                                  
## 2    202 118.96  1    13.045 22.151 4.674e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_SCI1, PHQ_SCI2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SCInz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    198 106.80                                  
## 2    197 100.47  1    6.3256 12.403 0.0005326 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_PS1, PHQ_PS2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ PSz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    200 93.784                                
## 2    199 89.041  1    4.7434 10.601 0.001328 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_wellbeing1, PHQ_wellbeing2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ wellbeingz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ wellbeingz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)   
## 1    200 82.936                                
## 2    199 79.259  1    3.6765 9.2309 0.002699 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_SC1, PHQ_SC2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ SCz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F   Pr(>F)    
## 1    203 166.34                                 
## 2    202 149.59  1    16.743 22.609 3.77e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(PHQ_perfectionism1, PHQ_perfectionism2)
## Analysis of Variance Table
## 
## Model 1: PHQz ~ perfectionismz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age
## Model 2: PHQz ~ perfectionismz + n_disability + student.status + n_orientation + 
##     n_ethnicity + n_gender + UGPG + n_year + age + SE_fi_z
##   Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
## 1    198 150.07                                  
## 2    197 135.15  1    14.927 21.759 5.696e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#### assumption check PHQ####
car::Anova( PHQ_aca2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.957   1  1.4523 0.2295670    
## SE_aca_z        22.291   1 33.8112 2.345e-08 ***
## n_disability     8.653   1 13.1239 0.0003688 ***
## student.status   2.436   2  1.8474 0.1603070    
## n_orientation    0.696   1  1.0559 0.3053728    
## n_ethnicity      1.095   1  1.6613 0.1988974    
## n_gender         1.308   2  0.9922 0.3725757    
## UGPG             0.592   1  0.8981 0.3444136    
## n_year           1.833   3  0.9269 0.4287166    
## age              2.253   1  3.4168 0.0659980 .  
## SE_fi_z          9.880   1 14.9855 0.0001462 ***
## Residuals      133.177 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_acc2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.957   1  1.4523 0.2295670    
## SE_acc_z        22.291   1 33.8112 2.345e-08 ***
## n_disability     8.653   1 13.1239 0.0003688 ***
## student.status   2.436   2  1.8474 0.1603070    
## n_orientation    0.696   1  1.0559 0.3053728    
## n_ethnicity      1.095   1  1.6613 0.1988974    
## n_gender         1.308   2  0.9922 0.3725757    
## UGPG             0.592   1  0.8981 0.3444136    
## n_year           1.833   3  0.9269 0.4287166    
## age              2.253   1  3.4168 0.0659980 .  
## SE_fi_z          9.880   1 14.9855 0.0001462 ***
## Residuals      133.177 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_co2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value   Pr(>F)    
## (Intercept)      2.533   1  3.4110 0.066287 .  
## SE_co_z          3.180   1  4.2820 0.039843 *  
## n_disability    15.790   1 21.2613 7.26e-06 ***
## student.status   1.062   2  0.7153 0.490309    
## n_orientation    0.247   1  0.3332 0.564427    
## n_ethnicity      0.526   1  0.7079 0.401182    
## n_gender         0.623   2  0.4191 0.658220    
## UGPG             0.173   1  0.2325 0.630220    
## n_year           1.698   3  0.7620 0.516675    
## age              6.492   1  8.7415 0.003497 ** 
## SE_fi_z         19.182   1 25.8299 8.74e-07 ***
## Residuals      144.073 194                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_fr2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.819   1  1.2633   0.26236    
## SE_fr_z         24.516   1 37.8163 4.091e-09 ***
## n_disability    16.501   1 25.4533 1.007e-06 ***
## student.status   4.907   2  3.7849   0.02434 *  
## n_orientation    0.075   1  0.1163   0.73339    
## n_ethnicity      0.359   1  0.5541   0.45750    
## n_gender         2.121   2  1.6355   0.19743    
## UGPG             0.049   1  0.0750   0.78444    
## n_year           2.399   3  1.2335   0.29862    
## age              3.369   1  5.1973   0.02367 *  
## SE_fi_z         19.625   1 30.2723 1.129e-07 ***
## Residuals      130.953 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_GAD2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                Sum Sq  Df  F value    Pr(>F)    
## (Intercept)     0.615   1   1.6143  0.205355    
## GADz           78.449   1 205.7467 < 2.2e-16 ***
## n_disability    1.813   1   4.7560  0.030352 *  
## student.status  0.539   2   0.7071  0.494279    
## n_orientation   0.050   1   0.1323  0.716443    
## n_ethnicity     0.446   1   1.1686  0.280975    
## n_gender        0.965   2   1.2654  0.284342    
## UGPG            0.002   1   0.0060  0.938288    
## n_year          2.660   3   2.3258  0.075936 .  
## age             1.448   1   3.7964  0.052748 .  
## SE_fi_z         3.708   1   9.7249  0.002083 ** 
## Residuals      77.020 202                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_CUDIT2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                Sum Sq Df F value    Pr(>F)    
## (Intercept)     0.070  1  0.1187  0.731323    
## CUDITz          0.649  1  1.0927  0.298794    
## n_disability    6.222  1 10.4787  0.001716 ** 
## student.status  3.783  2  3.1852  0.046292 *  
## n_orientation   0.746  1  1.2557  0.265578    
## n_ethnicity     0.014  1  0.0242  0.876798    
## n_gender        2.035  2  1.7136  0.186304    
## UGPG            0.960  1  1.6164  0.207024    
## n_year          0.437  3  0.2454  0.864425    
## age             1.001  1  1.6857  0.197638    
## SE_fi_z        10.953  1 18.4446 4.578e-05 ***
## Residuals      51.068 86                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_AUDIT2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      1.579   1  2.0634   0.15242    
## AUDITz           0.844   1  1.1027   0.29493    
## n_disability    14.909   1 19.4769 1.656e-05 ***
## student.status   2.550   2  1.6657   0.19165    
## n_orientation    0.447   1  0.5838   0.44570    
## n_ethnicity      0.439   1  0.5732   0.44986    
## n_gender         0.856   2  0.5593   0.57247    
## UGPG             0.290   1  0.3788   0.53896    
## n_year           2.002   3  0.8717   0.45667    
## age              4.355   1  5.6898   0.01799 *  
## SE_fi_z         21.181   1 27.6705 3.654e-07 ***
## Residuals      154.624 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_unil2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      1.412   1  2.4285 0.1207095    
## unil_z          38.052   1 65.4645 5.428e-14 ***
## n_disability     9.109   1 15.6709 0.0001044 ***
## student.status   3.102   2  2.6686 0.0717936 .  
## n_orientation    0.064   1  0.1095 0.7410390    
## n_ethnicity      0.288   1  0.4949 0.4825710    
## n_gender         1.880   2  1.6168 0.2010879    
## UGPG             0.841   1  1.4469 0.2304285    
## n_year           2.604   3  1.4933 0.2175256    
## age              4.745   1  8.1627 0.0047235 ** 
## SE_fi_z         14.548   1 25.0280 1.225e-06 ***
## Residuals      117.416 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_prel2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      1.998   1  2.9392 0.0879888 .  
## prel_z          18.164   1 26.7223 5.629e-07 ***
## n_disability     7.803   1 11.4801 0.0008457 ***
## student.status   2.324   2  1.7096 0.1835426    
## n_orientation    0.066   1  0.0977 0.7548746    
## n_ethnicity      0.033   1  0.0479 0.8269821    
## n_gender         1.765   2  1.2984 0.2752420    
## UGPG             0.557   1  0.8201 0.3662331    
## n_year           2.088   3  1.0239 0.3830712    
## age              5.396   1  7.9386 0.0053197 ** 
## SE_fi_z         22.438   1 33.0096 3.339e-08 ***
## Residuals      137.305 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_SA2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.482   1  0.8191 0.3665323    
## SAz             36.506   1 61.9886 2.075e-13 ***
## n_disability     9.221   1 15.6578 0.0001051 ***
## student.status   1.307   2  1.1097 0.3316698    
## n_orientation    0.141   1  0.2399 0.6248213    
## n_ethnicity      0.283   1  0.4807 0.4889028    
## n_gender         0.640   2  0.5430 0.5818342    
## UGPG             0.048   1  0.0819 0.7750797    
## n_year           3.653   3  2.0675 0.1057260    
## age              1.794   1  3.0458 0.0824652 .  
## SE_fi_z         13.045   1 22.1505 4.674e-06 ***
## Residuals      118.962 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_SCI2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df  F value    Pr(>F)    
## (Intercept)      0.409   1   0.8020 0.3715832    
## SCInz           52.677   1 103.2860 < 2.2e-16 ***
## n_disability     4.894   1   9.5951 0.0022358 ** 
## student.status   0.677   2   0.6633 0.5163183    
## n_orientation    0.005   1   0.0101 0.9201150    
## n_ethnicity      0.117   1   0.2293 0.6325704    
## n_gender         2.384   2   2.3377 0.0992220 .  
## UGPG             0.162   1   0.3179 0.5735076    
## n_year           1.297   3   0.8480 0.4691434    
## age              2.615   1   5.1268 0.0246488 *  
## SE_fi_z          6.326   1  12.4031 0.0005326 ***
## Residuals      100.471 197                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_PS2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                Sum Sq  Df  F value    Pr(>F)    
## (Intercept)     0.963   1   2.1514  0.144019    
## PSz            65.382   1 146.1258 < 2.2e-16 ***
## n_disability    2.664   1   5.9533  0.015566 *  
## student.status  0.287   2   0.3212  0.725627    
## n_orientation   0.023   1   0.0520  0.819909    
## n_ethnicity     0.072   1   0.1599  0.689679    
## n_gender        0.342   2   0.3820  0.682975    
## UGPG            0.209   1   0.4677  0.494825    
## n_year          0.118   3   0.0879  0.966594    
## age             1.762   1   3.9378  0.048586 *  
## SE_fi_z         4.743   1  10.6013  0.001328 ** 
## Residuals      89.041 199                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_wellbeing2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                Sum Sq  Df  F value    Pr(>F)    
## (Intercept)     0.042   1   0.1064  0.744592    
## wellbeingz     75.164   1 188.7182 < 2.2e-16 ***
## n_disability    3.254   1   8.1705  0.004711 ** 
## student.status  0.472   2   0.5921  0.554125    
## n_orientation   0.167   1   0.4204  0.517508    
## n_ethnicity     0.331   1   0.8314  0.362961    
## n_gender        1.420   2   1.7828  0.170849    
## UGPG            0.002   1   0.0039  0.950388    
## n_year          1.029   3   0.8613  0.462117    
## age             0.347   1   0.8701  0.352061    
## SE_fi_z         3.677   1   9.2309  0.002699 ** 
## Residuals      79.259 199                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_SC2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      1.699   1  2.2939  0.131445    
## SCz              5.874   1  7.9321  0.005338 ** 
## n_disability    14.694   1 19.8419 1.392e-05 ***
## student.status   3.012   2  2.0335  0.133547    
## n_orientation    0.281   1  0.3798  0.538404    
## n_ethnicity      0.267   1  0.3606  0.548871    
## n_gender         0.799   2  0.5396  0.583839    
## UGPG             0.296   1  0.3997  0.527959    
## n_year           1.932   3  0.8697  0.457712    
## age              4.149   1  5.6020  0.018886 *  
## SE_fi_z         16.743   1 22.6088 3.770e-06 ***
## Residuals      149.594 202                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
car::Anova(PHQ_perfectionism2, type=3)
## Anova Table (Type III tests)
## 
## Response: PHQz
##                 Sum Sq  Df F value    Pr(>F)    
## (Intercept)      0.824   1  1.2009 0.2744871    
## perfectionismz  18.003   1 26.2424 7.151e-07 ***
## n_disability     9.274   1 13.5182 0.0003046 ***
## student.status   2.348   2  1.7113 0.1833129    
## n_orientation    0.113   1  0.1649 0.6851006    
## n_ethnicity      0.309   1  0.4511 0.5026151    
## n_gender         1.590   2  1.1589 0.3159483    
## UGPG             0.534   1  0.7777 0.3789152    
## n_year           2.248   3  1.0921 0.3535849    
## age              3.223   1  4.6983 0.0313920 *  
## SE_fi_z         14.927   1 21.7595 5.696e-06 ***
## Residuals      135.145 197                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
hist(resid(PHQ_GAD2))

hist(resid(PHQ_CUDIT2))

hist(resid(PHQ_AUDIT2))

hist(resid(PHQ_unil2))

hist(resid(PHQ_prel2))

hist(resid(PHQ_SA2))

hist(resid(PHQ_SCI2))

hist(resid(PHQ_PS2))

hist(resid(PHQ_wellbeing2))

hist(resid(PHQ_SC2))

hist(resid(PHQ_perfectionism2))

hist(resid(PHQ_aca2))

hist(resid(PHQ_acc2))

hist(resid(PHQ_fr2))

hist(resid(PHQ_co2))

skewness(resid(PHQ_GAD2))
## [1] 0.3613142
skewness(resid(PHQ_CUDIT2))
## [1] 0.670844
skewness(resid(PHQ_AUDIT2))
## [1] 0.6533526
skewness(resid(PHQ_unil2))
## [1] 0.522866
skewness(resid(PHQ_prel2))
## [1] 0.4184681
skewness(resid(PHQ_SA2))
## [1] 0.5304959
skewness(resid(PHQ_SCI2))
## [1] 0.7159856
skewness(resid(PHQ_PS2))
## [1] 0.3384039
skewness(resid(PHQ_wellbeing2))
## [1] 0.3454778
skewness(resid(PHQ_SC2))
## [1] 0.7023051
skewness(resid(PHQ_perfectionism2))
## [1] 0.6348944
skewness(resid(PHQ_aca2))
## [1] 0.48578
skewness(resid(PHQ_acc2))
## [1] 0.48578
skewness(resid(PHQ_fr2))
## [1] 0.4543321
skewness(resid(PHQ_co2))
## [1] 0.5771592
qqPlot(PHQ_GAD2)

## [1] 157 230
qqPlot(PHQ_AUDIT2)

## [1] 141 228
qqPlot(PHQ_unil2)

## [1] 141 197
qqPlot(PHQ_prel2)

## [1] 141 197
qqPlot(PHQ_SA2)

## [1] 197 230
qqPlot(PHQ_SCI2)

## [1] 141 197
qqPlot(PHQ_PS2)

## [1] 141 197
qqPlot(PHQ_wellbeing2)

## [1] 30 88
qqPlot(PHQ_SC2)

## [1] 141 228
qqPlot(PHQ_perfectionism2)

## [1] 141 197
qqPlot(PHQ_aca2)

## [1] 141 197
qqPlot(PHQ_acc2)

## [1] 141 197
qqPlot(PHQ_fr2)

## [1]  25 197
qqPlot(PHQ_co2)

## [1] 141 197
a <- vif(PHQ_GAD2)
b <- vif(PHQ_CUDIT2)
c <- vif(PHQ_AUDIT2)
d <- vif(PHQ_unil2)
e <- vif(PHQ_prel2)
f <- vif(PHQ_SA2)
g <- vif(PHQ_SCI2)
h <- vif(PHQ_PS2)
i <- vif(PHQ_wellbeing2)
j <- vif(PHQ_SC2)
k <- vif(PHQ_perfectionism2)
l <- vif(PHQ_aca2)
m <- vif(PHQ_acc2)
n <- vif(PHQ_fr2)
o <- vif(PHQ_co2)

a
##                    GVIF Df GVIF^(1/(2*Df))
## GADz           1.362802  1        1.167391
## n_disability   1.374865  1        1.172546
## student.status 1.565043  2        1.118489
## n_orientation  1.154260  1        1.074365
## n_ethnicity    1.362961  1        1.167459
## n_gender       1.250283  2        1.057431
## UGPG           1.776581  1        1.332884
## n_year         1.499285  3        1.069828
## age            1.812170  1        1.346169
## SE_fi_z        1.213949  1        1.101793
b
##                    GVIF Df GVIF^(1/(2*Df))
## CUDITz         1.309466  1        1.144319
## n_disability   1.235014  1        1.111312
## student.status 1.473269  2        1.101718
## n_orientation  1.219652  1        1.104378
## n_ethnicity    1.394761  1        1.181000
## n_gender       1.405155  2        1.088757
## UGPG           2.246895  1        1.498965
## n_year         2.166743  3        1.137543
## age            1.825997  1        1.351295
## SE_fi_z        1.306770  1        1.143140
c
##                    GVIF Df GVIF^(1/(2*Df))
## AUDITz         1.064927  1        1.031953
## n_disability   1.278374  1        1.130652
## student.status 1.551259  2        1.116018
## n_orientation  1.145813  1        1.070427
## n_ethnicity    1.369165  1        1.170113
## n_gender       1.251139  2        1.057612
## UGPG           1.786080  1        1.336443
## n_year         1.459714  3        1.065070
## age            1.820758  1        1.349355
## SE_fi_z        1.117466  1        1.057103
d
##                    GVIF Df GVIF^(1/(2*Df))
## unil_z         1.098716  1        1.048197
## n_disability   1.298968  1        1.139723
## student.status 1.551578  2        1.116075
## n_orientation  1.147791  1        1.071350
## n_ethnicity    1.363038  1        1.167492
## n_gender       1.247271  2        1.056794
## UGPG           1.772715  1        1.331433
## n_year         1.453297  3        1.064288
## age            1.788667  1        1.337411
## SE_fi_z        1.133631  1        1.064721
e
##                    GVIF Df GVIF^(1/(2*Df))
## prel_z         1.286030  1        1.134033
## n_disability   1.344463  1        1.159510
## student.status 1.551097  2        1.115989
## n_orientation  1.203095  1        1.096857
## n_ethnicity    1.376138  1        1.173089
## n_gender       1.285717  2        1.064845
## UGPG           1.770194  1        1.330487
## n_year         1.475486  3        1.066979
## age            1.789427  1        1.337695
## SE_fi_z        1.114844  1        1.055862
f
##                    GVIF Df GVIF^(1/(2*Df))
## SAz            1.220849  1        1.104920
## n_disability   1.298849  1        1.139670
## student.status 1.559688  2        1.117531
## n_orientation  1.179619  1        1.086103
## n_ethnicity    1.363066  1        1.167504
## n_gender       1.238401  2        1.054910
## UGPG           1.777106  1        1.333081
## n_year         1.486672  3        1.068323
## age            1.826113  1        1.351338
## SE_fi_z        1.144716  1        1.069914
g
##                    GVIF Df GVIF^(1/(2*Df))
## SCInz          1.249145  1        1.117651
## n_disability   1.351606  1        1.162586
## student.status 1.571898  2        1.119711
## n_orientation  1.152501  1        1.073546
## n_ethnicity    1.372011  1        1.171329
## n_gender       1.247180  2        1.056774
## UGPG           1.769814  1        1.330343
## n_year         1.455428  3        1.064548
## age            1.782763  1        1.335202
## SE_fi_z        1.214412  1        1.102004
h
##                    GVIF Df GVIF^(1/(2*Df))
## PSz            1.348801  1        1.161379
## n_disability   1.367460  1        1.169384
## student.status 1.567793  2        1.118980
## n_orientation  1.145029  1        1.070060
## n_ethnicity    1.371249  1        1.171003
## n_gender       1.252204  2        1.057837
## UGPG           1.767469  1        1.329462
## n_year         1.475302  3        1.066957
## age            1.797619  1        1.340753
## SE_fi_z        1.214669  1        1.102120
i
##                    GVIF Df GVIF^(1/(2*Df))
## wellbeingz     1.321962  1        1.149766
## n_disability   1.344368  1        1.159469
## student.status 1.589420  2        1.122819
## n_orientation  1.141055  1        1.068202
## n_ethnicity    1.355534  1        1.164274
## n_gender       1.240061  2        1.055263
## UGPG           1.774370  1        1.332055
## n_year         1.456568  3        1.064687
## age            1.838201  1        1.355803
## SE_fi_z        1.221941  1        1.105414
j
##                    GVIF Df GVIF^(1/(2*Df))
## SCz            1.072830  1        1.035775
## n_disability   1.277648  1        1.130331
## student.status 1.552465  2        1.116235
## n_orientation  1.148564  1        1.071711
## n_ethnicity    1.364543  1        1.168137
## n_gender       1.238397  2        1.054909
## UGPG           1.771189  1        1.330860
## n_year         1.443574  3        1.063098
## age            1.799097  1        1.341304
## SE_fi_z        1.158847  1        1.076498
k
##                    GVIF Df GVIF^(1/(2*Df))
## perfectionismz 1.134169  1        1.064974
## n_disability   1.332899  1        1.154513
## student.status 1.554018  2        1.116514
## n_orientation  1.150180  1        1.072465
## n_ethnicity    1.370313  1        1.170604
## n_gender       1.246518  2        1.056634
## UGPG           1.767297  1        1.329397
## n_year         1.452111  3        1.064143
## age            1.787824  1        1.337095
## SE_fi_z        1.159032  1        1.076583
l
##                    GVIF Df GVIF^(1/(2*Df))
## SE_aca_z       1.394807  1        1.181019
## n_disability   1.319175  1        1.148554
## student.status 1.555608  2        1.116799
## n_orientation  1.141541  1        1.068429
## n_ethnicity    1.542722  1        1.242063
## n_gender       1.243054  2        1.055899
## UGPG           1.770416  1        1.330570
## n_year         1.458436  3        1.064914
## age            1.829124  1        1.352451
## SE_fi_z        1.209841  1        1.099928
m
##                    GVIF Df GVIF^(1/(2*Df))
## SE_acc_z       1.394807  1        1.181019
## n_disability   1.319175  1        1.148554
## student.status 1.555608  2        1.116799
## n_orientation  1.141541  1        1.068429
## n_ethnicity    1.542722  1        1.242063
## n_gender       1.243054  2        1.055899
## UGPG           1.770416  1        1.330570
## n_year         1.458436  3        1.064914
## age            1.829124  1        1.352451
## SE_fi_z        1.209841  1        1.099928
n
##                    GVIF Df GVIF^(1/(2*Df))
## SE_fr_z        1.081821  1        1.040106
## n_disability   1.280302  1        1.131504
## student.status 1.572642  2        1.119844
## n_orientation  1.150569  1        1.072646
## n_ethnicity    1.362886  1        1.167427
## n_gender       1.258821  2        1.059232
## UGPG           1.781047  1        1.334559
## n_year         1.460243  3        1.065134
## age            1.799517  1        1.341461
## SE_fi_z        1.116678  1        1.056730
o
##                    GVIF Df GVIF^(1/(2*Df))
## SE_co_z        1.127726  1        1.061944
## n_disability   1.301065  1        1.140642
## student.status 1.654736  2        1.134181
## n_orientation  1.145706  1        1.070377
## n_ethnicity    1.387863  1        1.178076
## n_gender       1.261269  2        1.059746
## UGPG           1.745027  1        1.320995
## n_year         1.470889  3        1.066424
## age            1.784916  1        1.336008
## SE_fi_z        1.113887  1        1.055408
ta <- 1/a
tb <- 1/b
tc <- 1/c
td <- 1/d
te <- 1/e
tf <- 1/f
tg <- 1/g
th <- 1/h
ti <- 1/i
tj <- 1/j
tk <- 1/k
tl <- 1/l
tm <- 1/m
tn <- 1/n
to <- 1/o

ta
##                     GVIF        Df GVIF^(1/(2*Df))
## GADz           0.7337821 1.0000000       0.8566108
## n_disability   0.7273441 1.0000000       0.8528447
## student.status 0.6389602 0.5000000       0.8940637
## n_orientation  0.8663556 1.0000000       0.9307822
## n_ethnicity    0.7336967 1.0000000       0.8565610
## n_gender       0.7998192 0.5000000       0.9456882
## UGPG           0.5628791 1.0000000       0.7502527
## n_year         0.6669847 0.3333333       0.9347296
## age            0.5518247 1.0000000       0.7428490
## SE_fi_z        0.8237580 1.0000000       0.9076112
tb
##                     GVIF        Df GVIF^(1/(2*Df))
## CUDITz         0.7636699 1.0000000       0.8738821
## n_disability   0.8097072 1.0000000       0.8998373
## student.status 0.6787625 0.5000000       0.9076731
## n_orientation  0.8199062 1.0000000       0.9054867
## n_ethnicity    0.7169689 1.0000000       0.8467402
## n_gender       0.7116655 0.5000000       0.9184785
## UGPG           0.4450586 1.0000000       0.6671271
## n_year         0.4615221 0.3333333       0.8790875
## age            0.5476459 1.0000000       0.7400310
## SE_fi_z        0.7652458 1.0000000       0.8747833
tc
##                     GVIF        Df GVIF^(1/(2*Df))
## AUDITz         0.9390314 1.0000000       0.9690363
## n_disability   0.7822439 1.0000000       0.8844455
## student.status 0.6446375 0.5000000       0.8960431
## n_orientation  0.8727424 1.0000000       0.9342068
## n_ethnicity    0.7303723 1.0000000       0.8546182
## n_gender       0.7992714 0.5000000       0.9455262
## UGPG           0.5598852 1.0000000       0.7482548
## n_year         0.6850658 0.3333333       0.9389059
## age            0.5492217 1.0000000       0.7410949
## SE_fi_z        0.8948814 1.0000000       0.9459817
td
##                     GVIF        Df GVIF^(1/(2*Df))
## unil_z         0.9101531 1.0000000       0.9540194
## n_disability   0.7698416 1.0000000       0.8774062
## student.status 0.6445052 0.5000000       0.8959971
## n_orientation  0.8712386 1.0000000       0.9334016
## n_ethnicity    0.7336554 1.0000000       0.8565368
## n_gender       0.8017504 0.5000000       0.9462585
## UGPG           0.5641064 1.0000000       0.7510702
## n_year         0.6880906 0.3333333       0.9395955
## age            0.5590755 1.0000000       0.7477135
## SE_fi_z        0.8821212 1.0000000       0.9392131
te
##                     GVIF        Df GVIF^(1/(2*Df))
## prel_z         0.7775865 1.0000000       0.8818087
## n_disability   0.7437916 1.0000000       0.8624336
## student.status 0.6447048 0.5000000       0.8960665
## n_orientation  0.8311896 1.0000000       0.9116960
## n_ethnicity    0.7266714 1.0000000       0.8524502
## n_gender       0.7777762 0.5000000       0.9391039
## UGPG           0.5649097 1.0000000       0.7516048
## n_year         0.6777426 0.3333333       0.9372256
## age            0.5588381 1.0000000       0.7475547
## SE_fi_z        0.8969866 1.0000000       0.9470938
tf
##                     GVIF        Df GVIF^(1/(2*Df))
## SAz            0.8191022 1.0000000       0.9050427
## n_disability   0.7699127 1.0000000       0.8774467
## student.status 0.6411538 0.5000000       0.8948301
## n_orientation  0.8477313 1.0000000       0.9207232
## n_ethnicity    0.7336403 1.0000000       0.8565281
## n_gender       0.8074927 0.5000000       0.9479483
## UGPG           0.5627127 1.0000000       0.7501418
## n_year         0.6726435 0.3333333       0.9360467
## age            0.5476112 1.0000000       0.7400076
## SE_fi_z        0.8735791 1.0000000       0.9346545
tg
##                     GVIF        Df GVIF^(1/(2*Df))
## SCInz          0.8005478 1.0000000       0.8947334
## n_disability   0.7398608 1.0000000       0.8601516
## student.status 0.6361737 0.5000000       0.8930873
## n_orientation  0.8676784 1.0000000       0.9314926
## n_ethnicity    0.7288574 1.0000000       0.8537314
## n_gender       0.8018090 0.5000000       0.9462758
## UGPG           0.5650313 1.0000000       0.7516856
## n_year         0.6870832 0.3333333       0.9393661
## age            0.5609270 1.0000000       0.7489506
## SE_fi_z        0.8234435 1.0000000       0.9074379
th
##                     GVIF        Df GVIF^(1/(2*Df))
## PSz            0.7413990 1.0000000       0.8610453
## n_disability   0.7312831 1.0000000       0.8551509
## student.status 0.6378393 0.5000000       0.8936713
## n_orientation  0.8733403 1.0000000       0.9345268
## n_ethnicity    0.7292624 1.0000000       0.8539686
## n_gender       0.7985920 0.5000000       0.9453252
## UGPG           0.5657806 1.0000000       0.7521839
## n_year         0.6778274 0.3333333       0.9372451
## age            0.5562913 1.0000000       0.7458494
## SE_fi_z        0.8232698 1.0000000       0.9073422
ti
##                     GVIF        Df GVIF^(1/(2*Df))
## wellbeingz     0.7564515 1.0000000       0.8697422
## n_disability   0.7438439 1.0000000       0.8624638
## student.status 0.6291604 0.5000000       0.8906157
## n_orientation  0.8763820 1.0000000       0.9361527
## n_ethnicity    0.7377168 1.0000000       0.8589044
## n_gender       0.8064122 0.5000000       0.9476310
## UGPG           0.5635802 1.0000000       0.7507198
## n_year         0.6865452 0.3333333       0.9392435
## age            0.5440102 1.0000000       0.7375705
## SE_fi_z        0.8183701 1.0000000       0.9046381
tj
##                     GVIF        Df GVIF^(1/(2*Df))
## SCz            0.9321145 1.0000000       0.9654608
## n_disability   0.7826883 1.0000000       0.8846968
## student.status 0.6441369 0.5000000       0.8958691
## n_orientation  0.8706520 1.0000000       0.9330874
## n_ethnicity    0.7328460 1.0000000       0.8560642
## n_gender       0.8074953 0.5000000       0.9479490
## UGPG           0.5645925 1.0000000       0.7513937
## n_year         0.6927253 0.3333333       0.9406474
## age            0.5558345 1.0000000       0.7455431
## SE_fi_z        0.8629267 1.0000000       0.9289385
tk
##                     GVIF        Df GVIF^(1/(2*Df))
## perfectionismz 0.8817026 1.0000000       0.9389902
## n_disability   0.7502442 1.0000000       0.8661664
## student.status 0.6434930 0.5000000       0.8956451
## n_orientation  0.8694290 1.0000000       0.9324318
## n_ethnicity    0.7297600 1.0000000       0.8542599
## n_gender       0.8022349 0.5000000       0.9464014
## UGPG           0.5658359 1.0000000       0.7522206
## n_year         0.6886526 0.3333333       0.9397234
## age            0.5593392 1.0000000       0.7478898
## SE_fi_z        0.8627893 1.0000000       0.9288645
tl
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_aca_z       0.7169452 1.0000000       0.8467262
## n_disability   0.7580493 1.0000000       0.8706603
## student.status 0.6428357 0.5000000       0.8954163
## n_orientation  0.8760092 1.0000000       0.9359536
## n_ethnicity    0.6482051 1.0000000       0.8051118
## n_gender       0.8044701 0.5000000       0.9470599
## UGPG           0.5648389 1.0000000       0.7515577
## n_year         0.6856658 0.3333333       0.9390429
## age            0.5467099 1.0000000       0.7393984
## SE_fi_z        0.8265546 1.0000000       0.9091505
tm
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_acc_z       0.7169452 1.0000000       0.8467262
## n_disability   0.7580493 1.0000000       0.8706603
## student.status 0.6428357 0.5000000       0.8954163
## n_orientation  0.8760092 1.0000000       0.9359536
## n_ethnicity    0.6482051 1.0000000       0.8051118
## n_gender       0.8044701 0.5000000       0.9470599
## UGPG           0.5648389 1.0000000       0.7515577
## n_year         0.6856658 0.3333333       0.9390429
## age            0.5467099 1.0000000       0.7393984
## SE_fi_z        0.8265546 1.0000000       0.9091505
tn
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_fr_z        0.9243671 1.0000000       0.9614401
## n_disability   0.7810660 1.0000000       0.8837794
## student.status 0.6358725 0.5000000       0.8929816
## n_orientation  0.8691353 1.0000000       0.9322742
## n_ethnicity    0.7337372 1.0000000       0.8565846
## n_gender       0.7943939 0.5000000       0.9440804
## UGPG           0.5614674 1.0000000       0.7493113
## n_year         0.6848177 0.3333333       0.9388492
## age            0.5557046 1.0000000       0.7454560
## SE_fi_z        0.8955134 1.0000000       0.9463157
to
##                     GVIF        Df GVIF^(1/(2*Df))
## SE_co_z        0.8867405 1.0000000       0.9416690
## n_disability   0.7686011 1.0000000       0.8766990
## student.status 0.6043260 0.5000000       0.8816939
## n_orientation  0.8728243 1.0000000       0.9342507
## n_ethnicity    0.7205320 1.0000000       0.8488416
## n_gender       0.7928521 0.5000000       0.9436220
## UGPG           0.5730570 1.0000000       0.7570053
## n_year         0.6798611 0.3333333       0.9377132
## age            0.5602503 1.0000000       0.7484987
## SE_fi_z        0.8977575 1.0000000       0.9475007
Single Linear Regressions
#### PHQ tables without finance and covariates####

PHQ_GADt1 <- export_summs(PHQ_GAD, scale = TRUE)
PHQ_CUDITt1 <- export_summs(PHQ_CUDIT,scale = TRUE)
PHQ_AUDITt1 <-export_summs(PHQ_AUDIT,scale = TRUE)
PHQ_unilt1 <- export_summs(PHQ_unil,scale = TRUE)
PHQ_prelt1 <- export_summs(PHQ_prel,scale = TRUE)
PHQ_SAt1 <- export_summs(PHQ_SA,scale = TRUE)
PHQ_SCIt1 <- export_summs(PHQ_SCI,scale = TRUE)
PHQ_PSt1 <- export_summs(PHQ_PS,scale = TRUE)
PHQ_SCt1 <- export_summs(PHQ_SC,scale = TRUE)
PHQ_wellbeingt1 <-export_summs(PHQ_wellbeing,scale = TRUE)
PHQ_perfectionismt1 <-export_summs(PHQ_perfectionism,scale = TRUE)
PHQ_acat1 <- export_summs(PHQ_aca,scale = TRUE)
PHQ_acct1 <- export_summs(PHQ_acc,scale = TRUE)
PHQ_frt1 <- export_summs(PHQ_fr,scale = TRUE)
PHQ_cot1 <- export_summs(PHQ_co,scale = TRUE)

PHQ_GADr1 <- PHQ_GADt1[7,2]
PHQ_CUDITr1 <- PHQ_CUDITt1[7,2]
PHQ_AUDITr1 <-PHQ_AUDITt1[7,2]
PHQ_unilr1 <- PHQ_unilt1[7,2]
PHQ_prelr1 <- PHQ_prelt1[7,2]
PHQ_SAr1 <- PHQ_SAt1[7,2]
PHQ_SCIr1 <- PHQ_SCIt1[7,2]
PHQ_PSr1 <- PHQ_PSt1[7,2]
PHQ_SCr1 <- PHQ_SCt1[7,2]
PHQ_wellbeingz1 <-PHQ_wellbeingt1[7,2]
PHQ_perfectionismz1 <-PHQ_perfectionismt1[7,2]
PHQ_acaz1 <- PHQ_acat1[7,2]
PHQ_accz1 <- PHQ_acct1[7,2]
PHQ_frz1 <- PHQ_frt1[7,2]
PHQ_coz1 <- PHQ_cot1[7,2]

PHQ_GADr1$model <- "GAD"
PHQ_CUDITr1$model <- "CUDIT"
PHQ_AUDITr1$model <- "AUDIT"
PHQ_unilr1$model <- "Unil"
PHQ_prelr1$model <- "Prel"
PHQ_SAr1$model <- "SA"
PHQ_SCIr1$model <- "SCI"
PHQ_PSr1$model <- "PS"
PHQ_SCr1$model <- "SC"
PHQ_wellbeingz1$model <- "wellbeing"
PHQ_perfectionismz1$model <-"perfectionism"
PHQ_acaz1$model <- "academic"
PHQ_accz1$model <- "accomodation"
PHQ_frz1$model <- "friendship"
PHQ_coz1$model <- "community"

rsquared_simp <- rbind(PHQ_GADr1,
                       PHQ_CUDITr1,PHQ_AUDITr1,PHQ_unilr1,
                       PHQ_prelr1,PHQ_SAr1,PHQ_SCIr1,
                       PHQ_PSr1,PHQ_SCr1,PHQ_wellbeingz1,PHQ_perfectionismz1,
                       PHQ_acaz1,PHQ_accz1,PHQ_frz1,PHQ_coz1)


PHQ_GADn1 <- PHQ_GADt1[6,2]
PHQ_CUDITn1 <- PHQ_CUDITt1[6,2]
PHQ_AUDITn1 <-PHQ_AUDITt1[6,2]
PHQ_uniln1 <- PHQ_unilt1[6,2]
PHQ_preln1 <- PHQ_prelt1[6,2]
PHQ_SAn1 <- PHQ_SAt1[6,2]
PHQ_SCIn1 <- PHQ_SCIt1[6,2]
PHQ_PSn1 <- PHQ_PSt1[6,2]
PHQ_SCn1 <- PHQ_SCt1[6,2]
PHQ_wellbeingn1 <-PHQ_wellbeingt1[6,2]
PHQ_perfectionismn1 <-PHQ_perfectionismt1[6,2]
PHQ_acan1 <- PHQ_acat1[6,2]
PHQ_accn1 <- PHQ_acct1[6,2]
PHQ_frn1 <- PHQ_frt1[6,2]
PHQ_con1 <- PHQ_cot1[6,2]

PHQ_GADn1$model <- "GAD"
PHQ_CUDITn1$model <- "CUDIT"
PHQ_AUDITn1$model <- "AUDIT"
PHQ_uniln1$model <- "Unil"
PHQ_preln1$model <- "Prel"
PHQ_SAn1$model <- "SA"
PHQ_SCIn1$model <- "SCI"
PHQ_PSn1$model <- "PS"
PHQ_SCn1$model <- "SC"
PHQ_wellbeingn1$model <- "wellbeing"
PHQ_perfectionismn1$model <-"perfectionism"
PHQ_acan1$model <- "academic"
PHQ_accn1$model <- "accomodation"
PHQ_frn1$model <- "friendship"
PHQ_con1$model <- "community"

n_simp <- rbind(PHQ_GADn1,
                PHQ_CUDITn1,PHQ_AUDITn1,PHQ_uniln1,
                PHQ_preln1,PHQ_SAn1,PHQ_SCIn1,
                PHQ_PSn1,PHQ_SCn1,PHQ_wellbeingn1,PHQ_perfectionismn1,
                PHQ_acan1,PHQ_accn1,PHQ_frn1,PHQ_con1)

colnames(rsquared_simp)<- c("rsquared", "model")
colnames(n_simp)<- c("n", "model")

all_models_simp <- rbind(
  tidy(PHQ_GAD) %>% mutate(model = "GAD"),
  tidy(PHQ_CUDIT) %>% mutate(model = "CUDIT"),
  tidy(PHQ_AUDIT) %>% mutate(model = "AUDIT"),
  tidy(PHQ_unil) %>% mutate(model = "Unil"),
  tidy(PHQ_prel) %>% mutate(model = "Prel"),
  tidy(PHQ_SA) %>% mutate(model = "SA"),
  tidy(PHQ_SCI) %>% mutate(model = "SCI"),
  tidy(PHQ_PS) %>% mutate(model = "PS"),
  tidy(PHQ_wellbeing) %>% mutate(model = "wellbeing"),
  tidy(PHQ_SC) %>% mutate(model = "SC"),
  tidy(PHQ_perfectionism) %>% mutate(model = "perfectionism"),
  tidy(PHQ_aca) %>% mutate(model = "academic"),
  tidy(PHQ_co) %>% mutate(model = "community"),
  tidy(PHQ_fr) %>% mutate(model = "friendship"),
  tidy(PHQ_acc) %>% mutate(model = "accomodation")
)


finaltable1_PHQ <- right_join( all_models_simp, rsquared_simp)
## Joining with `by = join_by(model)`
finaltable1_PHQ <- right_join( n_simp, finaltable1_PHQ)
## Joining with `by = join_by(model)`
## Warning in right_join(n_simp, finaltable1_PHQ): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable1_PHQ <- as.data.frame(finaltable1_PHQ)
finaltable1_PHQ <- finaltable1_PHQ[c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30),]
colnames(finaltable1_PHQ)<- c("N", "model","term", "beta", "SE", "t", "p", "rsquared" )
finaltable1_PHQ$beta <- round(finaltable1_PHQ$beta, digits = 2)
finaltable1_PHQ$rsquared <- as.numeric(finaltable1_PHQ$rsquared)
finaltable1_PHQ$rsquared <- round(finaltable1_PHQ$rsquared, digits = 2)
finaltable1_PHQ$p <- round(finaltable1_PHQ$p, digits = 3)
finaltable1_PHQ <- finaltable1_PHQ[,c(3,1,4,8,7)]

#### GAD tables without finance and covariates####
GAD_PHQt1 <- export_summs(GAD_PHQ, scale = TRUE)
GAD_CUDITt1 <- export_summs(GAD_CUDIT,scale = TRUE)
GAD_AUDITt1 <-export_summs(GAD_AUDIT,scale = TRUE)
GAD_unilt1 <- export_summs(GAD_unil,scale = TRUE)
GAD_prelt1 <- export_summs(GAD_prel,scale = TRUE)
GAD_SAt1 <- export_summs(GAD_SA,scale = TRUE)
GAD_SCIt1 <- export_summs(GAD_SCI,scale = TRUE)
GAD_PSt1 <- export_summs(GAD_PS,scale = TRUE)
GAD_SCt1 <- export_summs(GAD_SC,scale = TRUE)
GAD_wellbeingt1 <-export_summs(GAD_wellbeing,scale = TRUE)
GAD_perfectionismt1 <-export_summs(GAD_perfectionism,scale = TRUE)
GAD_acat1 <- export_summs(GAD_aca,scale = TRUE)
GAD_acct1 <- export_summs(GAD_acc,scale = TRUE)
GAD_frt1 <- export_summs(GAD_fr,scale = TRUE)
GAD_cot1 <- export_summs(GAD_co,scale = TRUE)

GAD_PHQr1 <- GAD_PHQt1[7,2]
GAD_CUDITr1 <- GAD_CUDITt1[7,2]
GAD_AUDITr1 <-GAD_AUDITt1[7,2]
GAD_unilr1 <- GAD_unilt1[7,2]
GAD_prelr1 <- GAD_prelt1[7,2]
GAD_SAr1 <- GAD_SAt1[7,2]
GAD_SCIr1 <- GAD_SCIt1[7,2]
GAD_PSr1 <- GAD_PSt1[7,2]
GAD_SCr1 <- GAD_SCt1[7,2]
GAD_wellbeingz1 <-GAD_wellbeingt1[7,2]
GAD_perfectionismz1 <-GAD_perfectionismt1[7,2]
GAD_acaz1 <- GAD_acat1[7,2]
GAD_accz1 <- GAD_acct1[7,2]
GAD_frz1 <- GAD_frt1[7,2]
GAD_coz1 <- GAD_cot1[7,2]

GAD_PHQr1$model <- "PHQ"
GAD_CUDITr1$model <- "CUDIT"
GAD_AUDITr1$model <- "AUDIT"
GAD_unilr1$model <- "Unil"
GAD_prelr1$model <- "Prel"
GAD_SAr1$model <- "SA"
GAD_SCIr1$model <- "SCI"
GAD_PSr1$model <- "PS"
GAD_SCr1$model <- "SC"
GAD_wellbeingz1$model <- "wellbeing"
GAD_perfectionismz1$model <-"perfectionism"
GAD_acaz1$model <- "academic"
GAD_accz1$model <- "accomodation"
GAD_frz1$model <- "friendship"
GAD_coz1$model <- "community"

rsquared_simp1 <- rbind(GAD_PHQr1,
                        GAD_CUDITr1,GAD_AUDITr1,GAD_unilr1,
                        GAD_prelr1,GAD_SAr1,GAD_SCIr1,
                        GAD_PSr1,GAD_SCr1,GAD_wellbeingz1,GAD_perfectionismz1,
                        GAD_acaz1,GAD_accz1,GAD_frz1,GAD_coz1)

GAD_PHQn1 <- GAD_PHQt1[6,2]
GAD_CUDITn1 <- GAD_CUDITt1[6,2]
GAD_AUDITn1 <-GAD_AUDITt1[6,2]
GAD_uniln1<- GAD_unilt1[6,2]
GAD_preln1 <- GAD_prelt1[6,2]
GAD_SAn1 <- GAD_SAt1[6,2]
GAD_SCIn1 <- GAD_SCIt1[6,2]
GAD_PSn1 <- GAD_PSt1[6,2]
GAD_SCn1 <- GAD_SCt1[6,2]
GAD_wellbeingn1 <-GAD_wellbeingt1[6,2]
GAD_perfectionismn1 <-GAD_perfectionismt1[6,2]
GAD_acan1 <- GAD_acat1[6,2]
GAD_accn1 <- GAD_acct1[6,2]
GAD_frn1 <- GAD_frt1[6,2]
GAD_con1 <- GAD_cot1[6,2]

GAD_PHQn1$model <- "PHQ"
GAD_CUDITn1$model <- "CUDIT"
GAD_AUDITn1$model <- "AUDIT"
GAD_uniln1$model <- "Unil"
GAD_preln1$model <- "Prel"
GAD_SAn1$model <- "SA"
GAD_SCIn1$model <- "SCI"
GAD_PSn1$model <- "PS"
GAD_SCn1$model <- "SC"
GAD_wellbeingn1$model <- "wellbeing"
GAD_perfectionismn1$model <-"perfectionism"
GAD_acan1$model <- "academic"
GAD_accn1$model <- "accomodation"
GAD_frn1$model <- "friendship"
GAD_con1$model <- "community"

n21 <- rbind(GAD_PHQn1,
             GAD_CUDITn1,GAD_AUDITn1,GAD_uniln1,
             GAD_preln1,GAD_SAn1,GAD_SCIn1,
             GAD_PSn1,GAD_SCn1,GAD_wellbeingn1,GAD_perfectionismn1,
             GAD_acan1,GAD_accn1,GAD_frn1,GAD_con1)

colnames(rsquared_simp1)<- c("rsquared", "model")
colnames(n21)<- c("n", "model")

all_models_simp1 <- rbind(
  tidy(GAD_PHQ) %>% mutate(model = "PHQ"),
  tidy(GAD_CUDIT) %>% mutate(model = "CUDIT"),
  tidy(GAD_AUDIT) %>% mutate(model = "AUDIT"),
  tidy(GAD_unil) %>% mutate(model = "Unil"),
  tidy(GAD_prel) %>% mutate(model = "Prel"),
  tidy(GAD_SA) %>% mutate(model = "SA"),
  tidy(GAD_SCI) %>% mutate(model = "SCI"),
  tidy(GAD_PS) %>% mutate(model = "PS"),
  tidy(GAD_wellbeing) %>% mutate(model = "wellbeing"),
  tidy(GAD_SC) %>% mutate(model = "SC"),
  tidy(GAD_perfectionism) %>% mutate(model = "perfectionism"),
  tidy(GAD_aca) %>% mutate(model = "academic"),
  tidy(GAD_co) %>% mutate(model = "community"),
  tidy(GAD_fr) %>% mutate(model = "friendship"),
  tidy(GAD_acc) %>% mutate(model = "accomodation")
)


finaltable1_GAD <- right_join( all_models_simp1, rsquared_simp1)
## Joining with `by = join_by(model)`
finaltable1_GAD <- right_join( n21, finaltable1_GAD)
## Joining with `by = join_by(model)`
## Warning in right_join(n21, finaltable1_GAD): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable1_GAD <- as.data.frame(finaltable1_GAD)
finaltable1_GAD <- finaltable1_GAD[c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30),]
colnames(finaltable1_GAD)<- c("N1", "model", "term", "beta1", "SE", "t", "p1", "rsquared1" )
finaltable1_GAD$beta1 <- round(finaltable1_GAD$beta1, digits = 2)
finaltable1_GAD$rsquared1 <- as.numeric(finaltable1_GAD$rsquared1)
finaltable1_GAD$rsquared1 <- round(finaltable1_GAD$rsquared1, digits = 2)
finaltable1_GAD$p1 <- round(finaltable1_GAD$p1, digits = 3)
finaltable1_GAD <- finaltable1_GAD[,c(3,1,4,8,7)]

PHQ <- as.data.frame(rbind(c("term","N", "beta",  "rsquared", "p"),
                           c("PHQz", "-","-","-", "-" ))
)
names(PHQ) <- PHQ[1,]
PHQ <- PHQ[- c(1),]

GAD <- as.data.frame(rbind(c("term", "N1","beta1",  "rsquared1", "p1"),
                           c("GADz","-", "-", "-", "-"))
)
names(GAD) <- GAD[1,]
GAD <- GAD[- c(1),]

# final table 1 (simple) ####
finaltable1_GAD <- rbind( GAD, finaltable1_GAD)
finaltable1_PHQ <- rbind(PHQ, finaltable1_PHQ)
final_table1 <- right_join(finaltable1_GAD,finaltable1_PHQ)
## Joining with `by = join_by(term)`
write.csv(final_table1, file = "final_table1.csv")

knitr::kable(final_table1, "pipe", caption = "Single Linear Regressions with scales as predictors")
Single Linear Regressions with scales as predictors
term N1 beta1 rsquared1 p1 N beta rsquared p
GADz - - - - 242 0.78 0.6 0
PHQz 242 0.77 0.6 0 - - - -
CUDITz 113 0.14 0.02 0.135 113 0.13 0.02 0.171
AUDITz 242 0.08 0.01 0.211 242 0.09 0.01 0.16
unil_z 242 0.42 0.17 0 242 0.5 0.24 0
prel_z 242 0.39 0.15 0 242 0.41 0.17 0
SAz 242 0.56 0.32 0 242 0.53 0.28 0
SCInz 236 0.59 0.35 0 236 0.66 0.43 0
PSz 238 0.68 0.46 0 238 0.73 0.52 0
SCz 242 -0.1 0.01 0.112 242 -0.25 0.06 0
wellbeingz 238 -0.68 0.46 0 238 -0.76 0.57 0
perfectionismz 236 0.41 0.17 0 236 0.39 0.15 0
SE_aca_z 242 -0.38 0.14 0 242 -0.43 0.18 0
SE_acc_z 242 -0.38 0.14 0 242 -0.43 0.18 0
SE_fr_z 242 -0.21 0.05 0.001 242 -0.3 0.09 0
SE_co_z 234 -0.23 0.06 0 234 -0.19 0.04 0.004
Multiple Linear Regression
### PHQ with just covariates ####
PHQ_GADt2 <- export_summs(PHQ_GAD1, scale = TRUE)
PHQ_CUDITt2 <- export_summs(PHQ_CUDIT1,scale = TRUE)
PHQ_AUDITt2 <-export_summs(PHQ_AUDIT1,scale = TRUE)
PHQ_unilt2 <- export_summs(PHQ_unil1,scale = TRUE)
PHQ_prelt2 <- export_summs(PHQ_prel1,scale = TRUE)
PHQ_SAt2 <- export_summs(PHQ_SA1,scale = TRUE)
PHQ_SCIt2 <- export_summs(PHQ_SCI1,scale = TRUE)
PHQ_PSt2 <- export_summs(PHQ_PS1,scale = TRUE)
PHQ_SCt2 <- export_summs(PHQ_SC1,scale = TRUE)
PHQ_wellbeingt2 <-export_summs(PHQ_wellbeing1,scale = TRUE)
PHQ_perfectionismt2 <-export_summs(PHQ_perfectionism1,scale = TRUE)
PHQ_acat2 <- export_summs(PHQ_aca1,scale = TRUE)
PHQ_acct2 <- export_summs(PHQ_acc1,scale = TRUE)
PHQ_frt2 <- export_summs(PHQ_fr1,scale = TRUE)
PHQ_cot2 <- export_summs(PHQ_co1,scale = TRUE)

PHQ_GADr2 <- PHQ_GADt2[31,2]
PHQ_CUDITr2 <- PHQ_CUDITt2[31,2]
PHQ_AUDITr2 <-PHQ_AUDITt2[31,2]
PHQ_unilr2 <- PHQ_unilt2[31,2]
PHQ_prelr2 <- PHQ_prelt2[31,2]
PHQ_SAr2 <- PHQ_SAt2[31,2]
PHQ_SCIr2 <- PHQ_SCIt2[31,2]
PHQ_PSr2 <- PHQ_PSt2[31,2]
PHQ_SCr2 <- PHQ_SCt2[31,2]
PHQ_wellbeingz2 <-PHQ_wellbeingt2[31,2]
PHQ_perfectionismz2 <-PHQ_perfectionismt2[31,2]
PHQ_acaz2 <- PHQ_acat2[31,2]
PHQ_accz2 <- PHQ_acct2[31,2]
PHQ_frz2 <- PHQ_frt2[31,2]
PHQ_coz2 <- PHQ_cot2[31,2]

PHQ_GADr2$model <- "GAD"
PHQ_CUDITr2$model <- "CUDIT"
PHQ_AUDITr2$model <- "AUDIT"
PHQ_unilr2$model <- "Unil"
PHQ_prelr2$model <- "Prel"
PHQ_SAr2$model <- "SA"
PHQ_SCIr2$model <- "SCI"
PHQ_PSr2$model <- "PS"
PHQ_SCr2$model <- "SC"
PHQ_wellbeingz2$model <- "wellbeing"
PHQ_perfectionismz2$model <-"perfectionism"
PHQ_acaz2$model <- "academic"
PHQ_accz2$model <- "accomodation"
PHQ_frz2$model <- "friendship"
PHQ_coz2$model <- "community"

rsquared_simp2 <- rbind(PHQ_GADr2,
                        PHQ_CUDITr2,PHQ_AUDITr2,PHQ_unilr2,
                        PHQ_prelr2,PHQ_SAr2,PHQ_SCIr2,
                        PHQ_PSr2,PHQ_SCr2,PHQ_wellbeingz2,PHQ_perfectionismz2,
                        PHQ_acaz2,PHQ_accz2,PHQ_frz2,PHQ_coz2)


PHQ_GADn2 <- PHQ_GADt2[30,2]
PHQ_CUDITn2 <- PHQ_CUDITt2[30,2]
PHQ_AUDITn2 <-PHQ_AUDITt2[30,2]
PHQ_uniln2 <- PHQ_unilt2[30,2]
PHQ_preln2 <- PHQ_prelt2[30,2]
PHQ_SAn2 <- PHQ_SAt2[30,2]
PHQ_SCIn2 <- PHQ_SCIt2[30,2]
PHQ_PSn2 <- PHQ_PSt2[30,2]
PHQ_SCn2 <- PHQ_SCt2[30,2]
PHQ_wellbeingn2 <-PHQ_wellbeingt2[30,2]
PHQ_perfectionismn2 <-PHQ_perfectionismt2[30,2]
PHQ_acan2 <- PHQ_acat2[30,2]
PHQ_accn2 <- PHQ_acct2[30,2]
PHQ_frn2 <- PHQ_frt2[30,2]
PHQ_con2 <- PHQ_cot2[30,2]

PHQ_GADn2$model <- "GAD"
PHQ_CUDITn2$model <- "CUDIT"
PHQ_AUDITn2$model <- "AUDIT"
PHQ_uniln2$model <- "Unil"
PHQ_preln2$model <- "Prel"
PHQ_SAn2$model <- "SA"
PHQ_SCIn2$model <- "SCI"
PHQ_PSn2$model <- "PS"
PHQ_SCn2$model <- "SC"
PHQ_wellbeingn2$model <- "wellbeing"
PHQ_perfectionismn2$model <-"perfectionism"
PHQ_acan2$model <- "academic"
PHQ_accn2$model <- "accomodation"
PHQ_frn2$model <- "friendship"
PHQ_con2$model <- "community"

n_simp2 <- rbind(PHQ_GADn2,
                 PHQ_CUDITn2,PHQ_AUDITn2,PHQ_uniln2,
                 PHQ_preln2,PHQ_SAn2,PHQ_SCIn2,
                 PHQ_PSn2,PHQ_SCn2,PHQ_wellbeingn2,PHQ_perfectionismn2,
                 PHQ_acan2,PHQ_accn2,PHQ_frn2,PHQ_con2)

colnames(rsquared_simp2)<- c("rsquared", "model")
colnames(n_simp2)<- c("n", "model")

all_models_simp2 <- rbind(
  tidy(PHQ_GAD1) %>% mutate(model = "GAD"),
  tidy(PHQ_CUDIT1) %>% mutate(model = "CUDIT"),
  tidy(PHQ_AUDIT1) %>% mutate(model = "AUDIT"),
  tidy(PHQ_unil1) %>% mutate(model = "Unil"),
  tidy(PHQ_prel1) %>% mutate(model = "Prel"),
  tidy(PHQ_SA1) %>% mutate(model = "SA"),
  tidy(PHQ_SCI1) %>% mutate(model = "SCI"),
  tidy(PHQ_PS1) %>% mutate(model = "PS"),
  tidy(PHQ_wellbeing1) %>% mutate(model = "wellbeing"),
  tidy(PHQ_SC1) %>% mutate(model = "SC"),
  tidy(PHQ_perfectionism1) %>% mutate(model = "perfectionism"),
  tidy(PHQ_aca1) %>% mutate(model = "academic"),
  tidy(PHQ_co1) %>% mutate(model = "community"),
  tidy(PHQ_fr1) %>% mutate(model = "friendship"),
  tidy(PHQ_acc1) %>% mutate(model = "accomodation")
)


finaltable_PHQ <- right_join( all_models_simp2, rsquared_simp2)
## Joining with `by = join_by(model)`
finaltable_PHQ <- right_join(n_simp2, finaltable_PHQ)
## Joining with `by = join_by(model)`
## Warning in right_join(n_simp2, finaltable_PHQ): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable_PHQ <- as.data.frame(finaltable_PHQ)
finaltable_PHQ <- finaltable_PHQ[c(2,16,30,44,58,72,86,100,114,128,142,156,170,184,198),]
colnames(finaltable_PHQ)<- c("N", "model","term", "beta", "SE", "t", 
                             "p", "rsquared" )
finaltable_PHQ$beta <- round(finaltable_PHQ$beta, digits = 2)
finaltable_PHQ$rsquared <- as.numeric(finaltable_PHQ$rsquared)
finaltable_PHQ$rsquared <- round(finaltable_PHQ$rsquared, digits = 2)
finaltable_PHQ$p <- round(finaltable_PHQ$p, digits = 3)
finaltable_PHQ <- finaltable_PHQ[,c(3,1,4,8,7)]

### GAD with just covariates####
GAD_PHQt2 <- export_summs(GAD_PHQ1, scale = TRUE)
GAD_CUDITt2 <- export_summs(GAD_CUDIT1,scale = TRUE)
GAD_AUDITt2 <-export_summs(GAD_AUDIT1,scale = TRUE)
GAD_unilt2 <- export_summs(GAD_unil1,scale = TRUE)
GAD_prelt2 <- export_summs(GAD_prel1,scale = TRUE)
GAD_SAt2 <- export_summs(GAD_SA1,scale = TRUE)
GAD_SCIt2 <- export_summs(GAD_SCI1,scale = TRUE)
GAD_PSt2 <- export_summs(GAD_PS1,scale = TRUE)
GAD_SCt2 <- export_summs(GAD_SC1,scale = TRUE)
GAD_wellbeingt2 <-export_summs(GAD_wellbeing1,scale = TRUE)
GAD_perfectionismt2 <-export_summs(GAD_perfectionism1,scale = TRUE)
GAD_acat2 <- export_summs(GAD_aca1,scale = TRUE)
GAD_acct2 <- export_summs(GAD_acc1,scale = TRUE)
GAD_frt2 <- export_summs(GAD_fr1,scale = TRUE)
GAD_cot2 <- export_summs(GAD_co1,scale = TRUE)

GAD_PHQr2 <- GAD_PHQt2[31,2]
GAD_CUDITr2 <- GAD_CUDITt2[31,2]
GAD_AUDITr2 <-GAD_AUDITt2[31,2]
GAD_unilr2 <- GAD_unilt2[31,2]
GAD_prelr2 <- GAD_prelt2[31,2]
GAD_SAr2 <- GAD_SAt2[31,2]
GAD_SCIr2 <- GAD_SCIt2[31,2]
GAD_PSr2 <- GAD_PSt2[31,2]
GAD_SCr2 <- GAD_SCt2[31,2]
GAD_wellbeingz2 <-GAD_wellbeingt2[31,2]
GAD_perfectionismz2 <-GAD_perfectionismt2[31,2]
GAD_acaz2 <- GAD_acat2[31,2]
GAD_accz2 <- GAD_acct2[31,2]
GAD_frz2 <- GAD_frt2[31,2]
GAD_coz2 <- GAD_cot2[31,2]

GAD_PHQr2$model <- "PHQ"
GAD_CUDITr2$model <- "CUDIT"
GAD_AUDITr2$model <- "AUDIT"
GAD_unilr2$model <- "Unil"
GAD_prelr2$model <- "Prel"
GAD_SAr2$model <- "SA"
GAD_SCIr2$model <- "SCI"
GAD_PSr2$model <- "PS"
GAD_SCr2$model <- "SC"
GAD_wellbeingz2$model <- "wellbeing"
GAD_perfectionismz2$model <-"perfectionism"
GAD_acaz2$model <- "academic"
GAD_accz2$model <- "accomodation"
GAD_frz2$model <- "friendship"
GAD_coz2$model <- "community"

rsquared_simp2 <- rbind(GAD_PHQr2,
                        GAD_CUDITr2,GAD_AUDITr2,GAD_unilr2,
                        GAD_prelr2,GAD_SAr2,GAD_SCIr2,
                        GAD_PSr2,GAD_SCr2,GAD_wellbeingz2,GAD_perfectionismz2,
                        GAD_acaz2,GAD_accz2,GAD_frz2,GAD_coz2)


GAD_PHQn2 <- GAD_PHQt2[30,2]
GAD_CUDITn2 <- GAD_CUDITt2[30,2]
GAD_AUDITn2 <-GAD_AUDITt2[30,2]
GAD_uniln2 <- GAD_unilt2[30,2]
GAD_preln2 <- GAD_prelt2[30,2]
GAD_SAn2 <- GAD_SAt2[30,2]
GAD_SCIn2 <- GAD_SCIt2[30,2]
GAD_PSn2 <- GAD_PSt2[30,2]
GAD_SCn2 <- GAD_SCt2[30,2]
GAD_wellbeingn2 <-GAD_wellbeingt2[30,2]
GAD_perfectionismn2 <-GAD_perfectionismt2[30,2]
GAD_acan2 <- GAD_acat2[30,2]
GAD_accn2 <- GAD_acct2[30,2]
GAD_frn2 <- GAD_frt2[30,2]
GAD_con2 <- GAD_cot2[30,2]

GAD_PHQn2$model <- "PHQ"
GAD_CUDITn2$model <- "CUDIT"
GAD_AUDITn2$model <- "AUDIT"
GAD_uniln2$model <- "Unil"
GAD_preln2$model <- "Prel"
GAD_SAn2$model <- "SA"
GAD_SCIn2$model <- "SCI"
GAD_PSn2$model <- "PS"
GAD_SCn2$model <- "SC"
GAD_wellbeingn2$model <- "wellbeing"
GAD_perfectionismn2$model <-"perfectionism"
GAD_acan2$model <- "academic"
GAD_accn2$model <- "accomodation"
GAD_frn2$model <- "friendship"
GAD_con2$model <- "community"

n_simp2 <- rbind(GAD_PHQn2,
                 GAD_CUDITn2,GAD_AUDITn2,GAD_uniln2,
                 GAD_preln2,GAD_SAn2,GAD_SCIn2,
                 GAD_PSn2,GAD_SCn2,GAD_wellbeingn2,GAD_perfectionismn2,
                 GAD_acan2,GAD_accn2,GAD_frn2,GAD_con2)

colnames(rsquared_simp2)<- c("rsquared", "model")
colnames(n_simp2)<- c("n", "model")

all_models_simp2 <- rbind(
  tidy(GAD_PHQ1) %>% mutate(model = "PHQ"),
  tidy(GAD_CUDIT1) %>% mutate(model = "CUDIT"),
  tidy(GAD_AUDIT1) %>% mutate(model = "AUDIT"),
  tidy(GAD_unil1) %>% mutate(model = "Unil"),
  tidy(GAD_prel1) %>% mutate(model = "Prel"),
  tidy(GAD_SA1) %>% mutate(model = "SA"),
  tidy(GAD_SCI1) %>% mutate(model = "SCI"),
  tidy(GAD_PS1) %>% mutate(model = "PS"),
  tidy(GAD_wellbeing1) %>% mutate(model = "wellbeing"),
  tidy(GAD_SC1) %>% mutate(model = "SC"),
  tidy(GAD_perfectionism1) %>% mutate(model = "perfectionism"),
  tidy(GAD_aca1) %>% mutate(model = "academic"),
  tidy(GAD_co1) %>% mutate(model = "community"),
  tidy(GAD_fr1) %>% mutate(model = "friendship"),
  tidy(GAD_acc1) %>% mutate(model = "accomodation")
)


finaltable_GAD <- right_join( all_models_simp2, rsquared_simp2)
## Joining with `by = join_by(model)`
finaltable_GAD <- right_join(n_simp2, finaltable_GAD)
## Joining with `by = join_by(model)`
## Warning in right_join(n_simp2, finaltable_GAD): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable_GAD <- as.data.frame(finaltable_GAD)
finaltable_GAD<- finaltable_GAD[c(2,16,30,44,58,72,86,100,114,128,142,156,170,184,198),]
colnames(finaltable_GAD)<- c("N1","Model", "term", "beta1", "SE1", "t1", "p1", 
                             "rsquared1" )
finaltable_GAD$beta1 <- round(finaltable_GAD$beta1, digits = 2)
finaltable_GAD$rsquared1 <- as.numeric(finaltable_GAD$rsquared1)
finaltable_GAD$rsquared1 <- round(finaltable_GAD$rsquared1, digits = 2)
finaltable_GAD$p1 <- round(finaltable_GAD$p1, digits = 3)
finaltable_GAD <- finaltable_GAD[,c(3,1,4,8,7)]

# final table (mulitple no finance)####
PHQ <- as.data.frame(rbind(c("term","N", "beta",  "rsquared", "p"),
                           c("PHQz", "-","-", "-", "-"))
)
names(PHQ) <- PHQ[1,]
PHQ <- PHQ[- c(1),]

GAD <- as.data.frame(rbind(c("term","N1", "beta1",  "rsquared1", "p1"),
                           c("GADz", "-", "-","-", "-"))
)
names(GAD) <- GAD[1,]
GAD <- GAD[- c(1),]

finaltable_GAD <- rbind( GAD, finaltable_GAD)
finaltable_PHQ <- rbind(PHQ, finaltable_PHQ)
final_table <- right_join(finaltable_GAD,finaltable_PHQ)
## Joining with `by = join_by(term)`
knitr::kable(final_table, "pipe", caption = "Multiple Linear Regression with demographics as covariates")
Multiple Linear Regression with demographics as covariates
term N1 beta1 rsquared1 p1 N beta rsquared p
GADz - - - - 217 0.73 0.63 0
PHQz 217 0.74 0.64 0 - - - -
CUDITz 101 0.09 0.28 0.402 101 0.04 0.3 0.663
AUDITz 217 0.11 0.21 0.093 217 0.08 0.2 0.214
unil_z 217 0.4 0.34 0 217 0.49 0.4 0
prel_z 217 0.33 0.28 0 217 0.33 0.28 0
SAz 217 0.5 0.42 0 217 0.48 0.4 0
SCInz 212 0.55 0.45 0 212 0.63 0.51 0
PSz 214 0.66 0.54 0 214 0.69 0.57 0
SCz 217 -0.07 0.21 0.294 217 -0.23 0.25 0
wellbeingz 214 -0.64 0.53 0 214 -0.72 0.62 0
perfectionismz 212 0.36 0.33 0 212 0.35 0.31 0
SE_aca_z 217 -0.42 0.33 0 217 -0.46 0.35 0
SE_acc_z 217 -0.42 0.33 0 217 -0.46 0.35 0
SE_fr_z 217 -0.25 0.26 0 217 -0.36 0.32 0
SE_co_z 209 -0.22 0.25 0.001 209 -0.15 0.23 0.027
Multiple Linear Regression with Finance
# table PHQ####
PHQ_GADt <- export_summs(PHQ_GAD2, scale = TRUE)
PHQ_CUDITt <- export_summs(PHQ_CUDIT2,scale = TRUE)
PHQ_AUDITt <-export_summs(PHQ_AUDIT2,scale = TRUE)
PHQ_unilt <- export_summs(PHQ_unil2,scale = TRUE)
PHQ_prelt <- export_summs(PHQ_prel2,scale = TRUE)
PHQ_SAt <- export_summs(PHQ_SA2,scale = TRUE)
PHQ_SCIt <- export_summs(PHQ_SCI2,scale = TRUE)
PHQ_PSt <- export_summs(PHQ_PS2,scale = TRUE)
PHQ_SCt <- export_summs(PHQ_SC2,scale = TRUE)
PHQ_wellbeingt <-export_summs(PHQ_wellbeing2,scale = TRUE)
PHQ_perfectionismt <-export_summs(PHQ_perfectionism2,scale = TRUE)
PHQ_acat <- export_summs(PHQ_aca2,scale = TRUE)
PHQ_acct <- export_summs(PHQ_acc2,scale = TRUE)
PHQ_frt <- export_summs(PHQ_fr2,scale = TRUE)
PHQ_cot <- export_summs(PHQ_co2,scale = TRUE)

PHQ_GADn <- PHQ_GADt[32,2]
PHQ_CUDITn <- PHQ_CUDITt[32,2]
PHQ_AUDITn <-PHQ_AUDITt[32,2]
PHQ_uniln <- PHQ_unilt[32,2]
PHQ_preln <- PHQ_prelt[32,2]
PHQ_SAn <- PHQ_SAt[32,2]
PHQ_SCIn <- PHQ_SCIt[32,2]
PHQ_PSn <- PHQ_PSt[32,2]
PHQ_SCn <- PHQ_SCt[32,2]
PHQ_wellbeingn <-PHQ_wellbeingt[32,2]
PHQ_perfectionismn <-PHQ_perfectionismt[32,2]
PHQ_acan <- PHQ_acat[32,2]
PHQ_accn <- PHQ_acct[32,2]
PHQ_frn <- PHQ_frt[32,2]
PHQ_con <- PHQ_cot[32,2]

PHQ_GADn$model <- "GAD"
PHQ_CUDITn$model <- "CUDIT"
PHQ_AUDITn$model <- "AUDIT"
PHQ_uniln$model <- "Unil"
PHQ_preln$model <- "Prel"
PHQ_SAn$model <- "SA"
PHQ_SCIn$model <- "SCI"
PHQ_PSn$model <- "PS"
PHQ_SCn$model <- "SC"
PHQ_wellbeingn$model <- "wellbeing"
PHQ_perfectionismn$model <-"perfectionism"
PHQ_acan$model <- "academic"
PHQ_accn$model <- "accomodation"
PHQ_frn$model <- "friendship"
PHQ_con$model <- "community"

PHQ_GADr <- PHQ_GADt[33,2]
PHQ_CUDITr <- PHQ_CUDITt[33,2]
PHQ_AUDITr <-PHQ_AUDITt[33,2]
PHQ_unilr <- PHQ_unilt[33,2]
PHQ_prelr <- PHQ_prelt[33,2]
PHQ_SAr <- PHQ_SAt[33,2]
PHQ_SCIr <- PHQ_SCIt[33,2]
PHQ_PSr <- PHQ_PSt[33,2]
PHQ_SCr <- PHQ_SCt[33,2]
PHQ_wellbeingz <-PHQ_wellbeingt[33,2]
PHQ_perfectionismz <-PHQ_perfectionismt[33,2]
PHQ_acaz <- PHQ_acat[33,2]
PHQ_accz <- PHQ_acct[33,2]
PHQ_frz <- PHQ_frt[33,2]
PHQ_coz <- PHQ_cot[33,2]


PHQ_GADr$model <- "GAD"
PHQ_CUDITr$model <- "CUDIT"
PHQ_AUDITr$model <- "AUDIT"
PHQ_unilr$model <- "Unil"
PHQ_prelr$model <- "Prel"
PHQ_SAr$model <- "SA"
PHQ_SCIr$model <- "SCI"
PHQ_PSr$model <- "PS"
PHQ_SCr$model <- "SC"
PHQ_wellbeingz$model <- "wellbeing"
PHQ_perfectionismz$model <-"perfectionism"
PHQ_acaz$model <- "academic"
PHQ_accz$model <- "accomodation"
PHQ_frz$model <- "friendship"
PHQ_coz$model <- "community"

n <- rbind(PHQ_GADn,
           PHQ_CUDITn,PHQ_AUDITn,PHQ_uniln,
           PHQ_preln,PHQ_SAn,PHQ_SCIn,
           PHQ_PSn,PHQ_SCn,PHQ_wellbeingn,PHQ_perfectionismn,
           PHQ_acan,PHQ_accn,PHQ_frn,PHQ_con)

rsquared <- rbind(PHQ_GADr,
                  PHQ_CUDITr,PHQ_AUDITr,PHQ_unilr,
                  PHQ_prelr,PHQ_SAr,PHQ_SCIr,
                  PHQ_PSr,PHQ_SCr,PHQ_wellbeingz,PHQ_perfectionismz,
                  PHQ_acaz,PHQ_accz,PHQ_frz,PHQ_coz)

colnames(rsquared)<- c("rsquared", "model")
colnames(n)<- c("n", "model")

all_models <- rbind(
  tidy(PHQ_GAD2) %>% mutate(model = "GAD"),
  tidy(PHQ_CUDIT2) %>% mutate(model = "CUDIT"),
  tidy(PHQ_AUDIT2) %>% mutate(model = "AUDIT"),
  tidy(PHQ_unil2) %>% mutate(model = "Unil"),
  tidy(PHQ_prel2) %>% mutate(model = "Prel"),
  tidy(PHQ_SA2) %>% mutate(model = "SA"),
  tidy(PHQ_SCI2) %>% mutate(model = "SCI"),
  tidy(PHQ_PS2) %>% mutate(model = "PS"),
  tidy(PHQ_SC2) %>% mutate(model = "SC"),
  tidy(PHQ_wellbeing2) %>% mutate(model = "wellbeing"),
  tidy(PHQ_perfectionism2) %>% mutate(model = "perfectionism"),
  tidy(PHQ_aca2) %>% mutate(model = "academic"),
  tidy(PHQ_co2) %>% mutate(model = "community"),
  tidy(PHQ_fr2) %>% mutate(model = "friendship"),
  tidy(PHQ_acc2) %>% mutate(model = "accomodation")
)


finaltable2_PHQ <- right_join( all_models, rsquared)
## Joining with `by = join_by(model)`
finaltable2_PHQ <- right_join(n, finaltable2_PHQ)
## Joining with `by = join_by(model)`
## Warning in right_join(n, finaltable2_PHQ): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable2_PHQ <- as.data.frame(finaltable2_PHQ)
finaltable2_PHQ <- finaltable2_PHQ[c(2,17,32,47,62,77,92,107,122,137,152,167,182,197,212),]
colnames(finaltable2_PHQ)<- c("N", "model", "term", "beta", "SE", "t", "p", "rsquared" )
finaltable2_PHQ$beta <- round(finaltable2_PHQ$beta, digits = 2)
finaltable2_PHQ$rsquared <- as.numeric(finaltable2_PHQ$rsquared)
finaltable2_PHQ$rsquared <- round(finaltable2_PHQ$rsquared, digits = 2)
finaltable2_PHQ$p <- round(finaltable2_PHQ$p, digits = 3)
finaltable2_PHQ <- finaltable2_PHQ[,c(3,1,4,8,7)]

# GAD tables####
GAD_PHQt <- export_summs(GAD_PHQ2, scale = TRUE)
GAD_CUDITt <- export_summs(GAD_CUDIT2,scale = TRUE)
GAD_AUDITt <-export_summs(GAD_AUDIT2,scale = TRUE)
GAD_unilt <- export_summs(GAD_unil2,scale = TRUE)
GAD_prelt <- export_summs(GAD_prel2,scale = TRUE)
GAD_SAt <- export_summs(GAD_SA2,scale = TRUE)
GAD_SCIt <- export_summs(GAD_SCI2,scale = TRUE)
GAD_PSt <- export_summs(GAD_PS2,scale = TRUE)
GAD_SCt <- export_summs(GAD_SC2,scale = TRUE)
GAD_wellbeingt <-export_summs(GAD_wellbeing2,scale = TRUE)
GAD_perfectionismt <-export_summs(GAD_perfectionism2,scale = TRUE)
GAD_acat <- export_summs(GAD_aca2,scale = TRUE)
GAD_acct <- export_summs(GAD_acc2,scale = TRUE)
GAD_frt <- export_summs(GAD_fr2,scale = TRUE)
GAD_cot <- export_summs(GAD_co2,scale = TRUE)

GAD_PHQr <- GAD_PHQt[33,2]
GAD_CUDITr <- GAD_CUDITt[33,2]
GAD_AUDITr <-GAD_AUDITt[33,2]
GAD_unilr <- GAD_unilt[33,2]
GAD_prelr <- GAD_prelt[33,2]
GAD_SAr <- GAD_SAt[33,2]
GAD_SCIr <- GAD_SCIt[33,2]
GAD_PSr <- GAD_PSt[33,2]
GAD_SCr <- GAD_SCt[33,2]
GAD_wellbeingz <-GAD_wellbeingt[33,2]
GAD_perfectionismz <-GAD_perfectionismt[33,2]
GAD_acaz <- GAD_acat[33,2]
GAD_accz <- GAD_acct[33,2]
GAD_frz <- GAD_frt[33,2]
GAD_coz <- GAD_cot[33,2]

GAD_PHQr$model <- "PHQ"
GAD_CUDITr$model <- "CUDIT"
GAD_AUDITr$model <- "AUDIT"
GAD_unilr$model <- "Unil"
GAD_prelr$model <- "Prel"
GAD_SAr$model <- "SA"
GAD_SCIr$model <- "SCI"
GAD_PSr$model <- "PS"
GAD_SCr$model <- "SC"
GAD_wellbeingz$model <- "wellbeing"
GAD_perfectionismz$model <-"perfectionism"
GAD_acaz$model <- "academic"
GAD_accz$model <- "accomodation"
GAD_frz$model <- "friendship"
GAD_coz$model <- "community"

rsquared2 <- rbind(GAD_PHQr,
                   GAD_CUDITr,GAD_AUDITr,GAD_unilr,
                   GAD_prelr,GAD_SAr,GAD_SCIr,
                   GAD_PSr,GAD_SCr,GAD_wellbeingz,GAD_perfectionismz,
                   GAD_acaz,GAD_accz,GAD_frz,GAD_coz)

GAD_PHQn <- GAD_PHQt[32,2]
GAD_CUDITn <- GAD_CUDITt[32,2]
GAD_AUDITn <-GAD_AUDITt[32,2]
GAD_uniln <- GAD_unilt[32,2]
GAD_preln <- GAD_prelt[32,2]
GAD_SAn <- GAD_SAt[32,2]
GAD_SCIn <- GAD_SCIt[32,2]
GAD_PSn <- GAD_PSt[32,2]
GAD_SCn <- GAD_SCt[32,2]
GAD_wellbeingn <-GAD_wellbeingt[32,2]
GAD_perfectionismn <-GAD_perfectionismt[32,2]
GAD_acan <- GAD_acat[32,2]
GAD_accn <- GAD_acct[32,2]
GAD_frn <- GAD_frt[32,2]
GAD_con <- GAD_cot[32,2]

GAD_PHQn$model <- "PHQ"
GAD_CUDITn$model <- "CUDIT"
GAD_AUDITn$model <- "AUDIT"
GAD_uniln$model <- "Unil"
GAD_preln$model <- "Prel"
GAD_SAn$model <- "SA"
GAD_SCIn$model <- "SCI"
GAD_PSn$model <- "PS"
GAD_SCn$model <- "SC"
GAD_wellbeingn$model <- "wellbeing"
GAD_perfectionismn$model <-"perfectionism"
GAD_acan$model <- "academic"
GAD_accn$model <- "accomodation"
GAD_frn$model <- "friendship"
GAD_con$model <- "community"

n2 <- rbind(GAD_PHQn,
            GAD_CUDITn,GAD_AUDITn,GAD_uniln,
            GAD_preln,GAD_SAn,GAD_SCIn,
            GAD_PSn,GAD_SCn,GAD_wellbeingn,GAD_perfectionismn,
            GAD_acan,GAD_accn,GAD_frn,GAD_con)

colnames(rsquared2)<- c("rsquared", "model")
colnames(n2)<- c("n", "model")

all_models2 <- rbind(
  tidy(GAD_PHQ2) %>% mutate(model = "PHQ"),
  tidy(GAD_CUDIT2) %>% mutate(model = "CUDIT"),
  tidy(GAD_AUDIT2) %>% mutate(model = "AUDIT"),
  tidy(GAD_unil2) %>% mutate(model = "Unil"),
  tidy(GAD_prel2) %>% mutate(model = "Prel"),
  tidy(GAD_SA2) %>% mutate(model = "SA"),
  tidy(GAD_SCI2) %>% mutate(model = "SCI"),
  tidy(GAD_PS2) %>% mutate(model = "PS"),
  tidy(GAD_SC2) %>% mutate(model = "SC"),
  tidy(GAD_wellbeing2) %>% mutate(model = "wellbeing"),
  tidy(GAD_perfectionism2) %>% mutate(model = "perfectionism"),
  tidy(GAD_aca2) %>% mutate(model = "academic"),
  tidy(GAD_co2) %>% mutate(model = "community"),
  tidy(GAD_fr2) %>% mutate(model = "friendship"),
  tidy(GAD_acc2) %>% mutate(model = "accomodation")
)


finaltable2_GAD <- right_join( all_models2, rsquared2)
## Joining with `by = join_by(model)`
finaltable2_GAD <- right_join( n2, finaltable2_GAD)
## Joining with `by = join_by(model)`
## Warning in right_join(n2, finaltable2_GAD): Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 1 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.
finaltable2_GAD <- as.data.frame(finaltable2_GAD)
finaltable2_GAD <- finaltable2_GAD[c(2,17,32,47,62,77,92,107,122,137,152,167,182,197,212),]
colnames(finaltable2_GAD)<- c("N1", "model", "term", "beta1", "SE", "t", "p1", "rsquared1" )
finaltable2_GAD$beta1 <- round(finaltable2_GAD$beta1, digits = 2)
finaltable2_GAD$rsquared1 <- as.numeric(finaltable2_GAD$rsquared1)
finaltable2_GAD$rsquared1 <- round(finaltable2_GAD$rsquared1, digits = 2)
finaltable2_GAD$p1 <- round(finaltable2_GAD$p1, digits = 3)
finaltable2_GAD <- finaltable2_GAD[,c(3,1,4,8,7)]

# final table 2 (mulitple finance)####



finaltable2_GAD <- rbind( GAD, finaltable2_GAD)
finaltable2_PHQ <- rbind(PHQ, finaltable2_PHQ)
final_table2 <- right_join(finaltable2_GAD,finaltable2_PHQ)
## Joining with `by = join_by(term)`
knitr::kable(final_table2, "pipe", caption = "Multiple Linear regression with demographics & finance as covariates")
Multiple Linear regression with demographics & finance as covariates
term N1 beta1 rsquared1 p1 N beta rsquared p
GADz - - - - 217 0.69 0.65 0
PHQz 217 0.73 0.64 0 - - - -
CUDITz 101 0.15 0.45 0.108 101 0.09 0.42 0.299
AUDITz 217 0.1 0.27 0.127 217 0.06 0.3 0.295
unil_z 217 0.37 0.38 0 217 0.45 0.47 0
prel_z 217 0.34 0.35 0 217 0.34 0.38 0
SAz 217 0.47 0.45 0 217 0.44 0.46 0
SCInz 212 0.51 0.46 0 212 0.57 0.54 0
PSz 214 0.63 0.55 0 214 0.65 0.59 0
SCz 217 -0.02 0.27 0.791 217 -0.17 0.32 0.005
wellbeingz 214 -0.61 0.54 0 214 -0.68 0.64 0
perfectionismz 212 0.33 0.36 0 212 0.3 0.38 0
SE_aca_z 217 -0.37 0.36 0 217 -0.39 0.4 0
SE_acc_z 217 -0.37 0.36 0 217 -0.39 0.4 0
SE_fr_z 217 -0.24 0.32 0 217 -0.35 0.41 0
SE_co_z 209 -0.21 0.3 0.002 209 -0.13 0.32 0.04
DNA and Medical data
wave1$Q207 <- dplyr::recode(wave1$Q207, "1" = "Yes", "2" = "No")
wave1$DNA <- "Not Answered"
wave1$DNA[wave1$Q207 == "Yes"] <- "Yes"
wave1$DNA[wave1$Q207 == "No"] <- "No"
table(wave1$DNA)
## 
##           No Not Answered          Yes 
##           50          260          134
#          No Not Answered          Yes 
#         50          260           134
prop.table(table(wave1$DNA))
## 
##           No Not Answered          Yes 
##    0.1126126    0.5855856    0.3018018
pass$Q207 <- dplyr::recode(pass$Q207, "1" = "Yes", "2" = "No")
pass$DNA <- "Not Answered"
pass$DNA[pass$Q207 == "Yes"] <- "Yes"
pass$DNA[pass$Q207 == "No"] <- "No"
table(pass$DNA)
## 
##           No Not Answered          Yes 
##           49           60          133
# No      Not Answered      Yes 
# 49          60           133 
prop.table(table(pass$DNA))
## 
##           No Not Answered          Yes 
##    0.2024793    0.2479339    0.5495868
# No      Not Answered     Yes 
# 0.20        0.25        0.55

wave1$Q209 <- dplyr::recode(wave1$Q209, "1" = "Yes", "2" = "No")
wave1$medicine <- "Not Answered"
wave1$medicine[wave1$Q209 == "Yes"] <- "Yes"
wave1$medicine[wave1$Q209 == "No"] <- "No"
table(wave1$medicine)
## 
##           No Not Answered          Yes 
##           73          260          111
#          No     Not Answered      Yes 
#.         73          260          111 
prop.table(table(wave1$medicine))
## 
##           No Not Answered          Yes 
##    0.1644144    0.5855856    0.2500000
#    No     Not Answered        Yes 
#  0.16       0.59             0.25

pass$Q209 <- dplyr::recode(pass$Q209, "1" = "Yes", "2" = "No")
pass$medicine <- "Not Answered"
pass$medicine[pass$Q209 == "Yes"] <- "Yes"
pass$medicine[pass$Q209 == "No"] <- "No"
table(pass$medicine)
## 
##           No Not Answered          Yes 
##           71           60          111
# No      Not Answered          Yes 
# 71           60               111 
prop.table(table(pass$medicine))
## 
##           No Not Answered          Yes 
##    0.2933884    0.2479339    0.4586777
# No      Not Answered         Yes 
# 0.30    0.25                 0.45